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.gitattributes

@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.ipynb filter=lfs diff=lfs merge=lfs -text
+omnifall_dataset_examples.ipynb filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,5 +1,3 @@
 convert_oops_via_to_csv.py
-# Symlink for local testing (HF derives dataset name from directory name)
 .claude
-hf.py
 __pycache__

README.md

@@ -12,6 +12,1985 @@ tags:
 pretty_name: 'OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection'
 size_categories:
 - 10K<n<100K
+configs:
+- config_name: labels
+  data_files:
+  - split: train
+    path: parquet/labels/train-*.parquet
+  default: true
+- config_name: labels-syn
+  data_files:
+  - split: train
+    path: parquet/labels-syn/train-*.parquet
+- config_name: metadata-syn
+  data_files:
+  - split: train
+    path: parquet/metadata-syn/train-*.parquet
+- config_name: of-sta-cs
+  data_files:
+  - split: train
+    path: parquet/of-sta-cs/train-*.parquet
+  - split: validation
+    path: parquet/of-sta-cs/validation-*.parquet
+  - split: test
+    path: parquet/of-sta-cs/test-*.parquet
+- config_name: of-sta-cv
+  data_files:
+  - split: train
+    path: parquet/of-sta-cv/train-*.parquet
+  - split: validation
+    path: parquet/of-sta-cv/validation-*.parquet
+  - split: test
+    path: parquet/of-sta-cv/test-*.parquet
+- config_name: of-itw
+  data_files:
+  - split: train
+    path: parquet/of-itw/train-*.parquet
+  - split: validation
+    path: parquet/of-itw/validation-*.parquet
+  - split: test
+    path: parquet/of-itw/test-*.parquet
+- config_name: of-syn
+  data_files:
+  - split: train
+    path: parquet/of-syn/train-*.parquet
+  - split: validation
+    path: parquet/of-syn/validation-*.parquet
+  - split: test
+    path: parquet/of-syn/test-*.parquet
+- config_name: of-syn-cross-age
+  data_files:
+  - split: train
+    path: parquet/of-syn-cross-age/train-*.parquet
+  - split: validation
+    path: parquet/of-syn-cross-age/validation-*.parquet
+  - split: test
+    path: parquet/of-syn-cross-age/test-*.parquet
+- config_name: of-syn-cross-ethnicity
+  data_files:
+  - split: train
+    path: parquet/of-syn-cross-ethnicity/train-*.parquet
+  - split: validation
+    path: parquet/of-syn-cross-ethnicity/validation-*.parquet
+  - split: test
+    path: parquet/of-syn-cross-ethnicity/test-*.parquet
+- config_name: of-syn-cross-bmi
+  data_files:
+  - split: train
+    path: parquet/of-syn-cross-bmi/train-*.parquet
+  - split: validation
+    path: parquet/of-syn-cross-bmi/validation-*.parquet
+  - split: test
+    path: parquet/of-syn-cross-bmi/test-*.parquet
+- config_name: of-sta-itw-cs
+  data_files:
+  - split: train
+    path: parquet/of-sta-itw-cs/train-*.parquet
+  - split: validation
+    path: parquet/of-sta-itw-cs/validation-*.parquet
+  - split: test
+    path: parquet/of-sta-itw-cs/test-*.parquet
+- config_name: of-sta-itw-cv
+  data_files:
+  - split: train
+    path: parquet/of-sta-itw-cv/train-*.parquet
+  - split: validation
+    path: parquet/of-sta-itw-cv/validation-*.parquet
+  - split: test
+    path: parquet/of-sta-itw-cv/test-*.parquet
+- config_name: of-syn-itw
+  data_files:
+  - split: train
+    path: parquet/of-syn-itw/train-*.parquet
+  - split: validation
+    path: parquet/of-syn-itw/validation-*.parquet
+  - split: test
+    path: parquet/of-syn-itw/test-*.parquet
+- config_name: cs
+  data_files:
+  - split: train
+    path: parquet/cs/train-*.parquet
+  - split: validation
+    path: parquet/cs/validation-*.parquet
+  - split: test
+    path: parquet/cs/test-*.parquet
+- config_name: cv
+  data_files:
+  - split: train
+    path: parquet/cv/train-*.parquet
+  - split: validation
+    path: parquet/cv/validation-*.parquet
+  - split: test
+    path: parquet/cv/test-*.parquet
+- config_name: caucafall
+  data_files:
+  - split: train
+    path: parquet/caucafall/train-*.parquet
+  - split: validation
+    path: parquet/caucafall/validation-*.parquet
+  - split: test
+    path: parquet/caucafall/test-*.parquet
+- config_name: cmdfall
+  data_files:
+  - split: train
+    path: parquet/cmdfall/train-*.parquet
+  - split: validation
+    path: parquet/cmdfall/validation-*.parquet
+  - split: test
+    path: parquet/cmdfall/test-*.parquet
+- config_name: edf
+  data_files:
+  - split: train
+    path: parquet/edf/train-*.parquet
+  - split: validation
+    path: parquet/edf/validation-*.parquet
+  - split: test
+    path: parquet/edf/test-*.parquet
+- config_name: gmdcsa24
+  data_files:
+  - split: train
+    path: parquet/gmdcsa24/train-*.parquet
+  - split: validation
+    path: parquet/gmdcsa24/validation-*.parquet
+  - split: test
+    path: parquet/gmdcsa24/test-*.parquet
+- config_name: le2i
+  data_files:
+  - split: train
+    path: parquet/le2i/train-*.parquet
+  - split: validation
+    path: parquet/le2i/validation-*.parquet
+  - split: test
+    path: parquet/le2i/test-*.parquet
+- config_name: mcfd
+  data_files:
+  - split: train
+    path: parquet/mcfd/train-*.parquet
+- config_name: occu
+  data_files:
+  - split: train
+    path: parquet/occu/train-*.parquet
+  - split: validation
+    path: parquet/occu/validation-*.parquet
+  - split: test
+    path: parquet/occu/test-*.parquet
+- config_name: up_fall
+  data_files:
+  - split: train
+    path: parquet/up_fall/train-*.parquet
+  - split: validation
+    path: parquet/up_fall/validation-*.parquet
+  - split: test
+    path: parquet/up_fall/test-*.parquet
+- config_name: cs-staged
+  data_files:
+  - split: train
+    path: parquet/cs-staged/train-*.parquet
+  - split: validation
+    path: parquet/cs-staged/validation-*.parquet
+  - split: test
+    path: parquet/cs-staged/test-*.parquet
+- config_name: cv-staged
+  data_files:
+  - split: train
+    path: parquet/cv-staged/train-*.parquet
+  - split: validation
+    path: parquet/cv-staged/validation-*.parquet
+  - split: test
+    path: parquet/cv-staged/test-*.parquet
+- config_name: cs-staged-wild
+  data_files:
+  - split: train
+    path: parquet/cs-staged-wild/train-*.parquet
+  - split: validation
+    path: parquet/cs-staged-wild/validation-*.parquet
+  - split: test
+    path: parquet/cs-staged-wild/test-*.parquet
+- config_name: cv-staged-wild
+  data_files:
+  - split: train
+    path: parquet/cv-staged-wild/train-*.parquet
+  - split: validation
+    path: parquet/cv-staged-wild/validation-*.parquet
+  - split: test
+    path: parquet/cv-staged-wild/test-*.parquet
+- config_name: OOPS
+  data_files:
+  - split: train
+    path: parquet/OOPS/train-*.parquet
+  - split: validation
+    path: parquet/OOPS/validation-*.parquet
+  - split: test
+    path: parquet/OOPS/test-*.parquet
+dataset_info:
+- config_name: labels
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 52618
+- config_name: labels-syn
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 19228
+- config_name: metadata-syn
+  features:
+  - name: path
+    dtype: string
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 12000
+- config_name: of-sta-cs
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 26036
+  - name: validation
+    num_bytes: 0
+    num_examples: 4272
+  - name: test
+    num_bytes: 0
+    num_examples: 18664
+- config_name: of-sta-cv
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 8888
+  - name: validation
+    num_bytes: 0
+    num_examples: 8185
+  - name: test
+    num_bytes: 0
+    num_examples: 27199
+- config_name: of-itw
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 1023
+  - name: validation
+    num_bytes: 0
+    num_examples: 482
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: of-syn
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 15344
+  - name: validation
+    num_bytes: 0
+    num_examples: 1956
+  - name: test
+    num_bytes: 0
+    num_examples: 1928
+- config_name: of-syn-cross-age
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 6364
+  - name: validation
+    num_bytes: 0
+    num_examples: 3183
+  - name: test
+    num_bytes: 0
+    num_examples: 9681
+- config_name: of-syn-cross-ethnicity
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 8267
+  - name: validation
+    num_bytes: 0
+    num_examples: 2762
+  - name: test
+    num_bytes: 0
+    num_examples: 8199
+- config_name: of-syn-cross-bmi
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  - name: age_group
+    dtype:
+      class_label:
+        names:
+          '0': toddlers_1_4
+          '1': children_5_12
+          '2': teenagers_13_17
+          '3': young_adults_18_34
+          '4': middle_aged_35_64
+          '5': elderly_65_plus
+  - name: gender_presentation
+    dtype:
+      class_label:
+        names:
+          '0': male
+          '1': female
+  - name: monk_skin_tone
+    dtype:
+      class_label:
+        names:
+          '0': mst1
+          '1': mst2
+          '2': mst3
+          '3': mst4
+          '4': mst5
+          '5': mst6
+          '6': mst7
+          '7': mst8
+          '8': mst9
+          '9': mst10
+  - name: race_ethnicity_omb
+    dtype:
+      class_label:
+        names:
+          '0': white
+          '1': black
+          '2': asian
+          '3': hispanic_latino
+          '4': aian
+          '5': nhpi
+          '6': mena
+  - name: bmi_band
+    dtype:
+      class_label:
+        names:
+          '0': underweight
+          '1': normal
+          '2': overweight
+          '3': obese
+  - name: height_band
+    dtype:
+      class_label:
+        names:
+          '0': short
+          '1': avg
+          '2': tall
+  - name: environment_category
+    dtype:
+      class_label:
+        names:
+          '0': indoor
+          '1': outdoor
+  - name: camera_shot
+    dtype:
+      class_label:
+        names:
+          '0': static_wide
+          '1': static_medium_wide
+  - name: speed
+    dtype:
+      class_label:
+        names:
+          '0': 24fps_rt
+          '1': 25fps_rt
+          '2': 30fps_rt
+          '3': std_rt
+  - name: camera_elevation
+    dtype:
+      class_label:
+        names:
+          '0': eye
+          '1': low
+          '2': high
+          '3': top
+  - name: camera_azimuth
+    dtype:
+      class_label:
+        names:
+          '0': front
+          '1': rear
+          '2': left
+          '3': right
+  - name: camera_distance
+    dtype:
+      class_label:
+        names:
+          '0': medium
+          '1': far
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 9675
+  - name: validation
+    num_bytes: 0
+    num_examples: 4701
+  - name: test
+    num_bytes: 0
+    num_examples: 4852
+- config_name: of-sta-itw-cs
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 26036
+  - name: validation
+    num_bytes: 0
+    num_examples: 4272
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: of-sta-itw-cv
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 8888
+  - name: validation
+    num_bytes: 0
+    num_examples: 8185
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: of-syn-itw
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 15344
+  - name: validation
+    num_bytes: 0
+    num_examples: 1956
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: cs
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 27059
+  - name: validation
+    num_bytes: 0
+    num_examples: 4754
+  - name: test
+    num_bytes: 0
+    num_examples: 22337
+- config_name: cv
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 9911
+  - name: validation
+    num_bytes: 0
+    num_examples: 8667
+  - name: test
+    num_bytes: 0
+    num_examples: 30872
+- config_name: caucafall
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 176
+  - name: validation
+    num_bytes: 0
+    num_examples: 25
+  - name: test
+    num_bytes: 0
+    num_examples: 47
+- config_name: cmdfall
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 20884
+  - name: validation
+    num_bytes: 0
+    num_examples: 3689
+  - name: test
+    num_bytes: 0
+    num_examples: 17570
+- config_name: edf
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 302
+  - name: validation
+    num_bytes: 0
+    num_examples: 78
+  - name: test
+    num_bytes: 0
+    num_examples: 128
+- config_name: gmdcsa24
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 213
+  - name: validation
+    num_bytes: 0
+    num_examples: 152
+  - name: test
+    num_bytes: 0
+    num_examples: 93
+- config_name: le2i
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 670
+  - name: validation
+    num_bytes: 0
+    num_examples: 94
+  - name: test
+    num_bytes: 0
+    num_examples: 203
+- config_name: mcfd
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 1352
+- config_name: occu
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 289
+  - name: validation
+    num_bytes: 0
+    num_examples: 94
+  - name: test
+    num_bytes: 0
+    num_examples: 101
+- config_name: up_fall
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 2150
+  - name: validation
+    num_bytes: 0
+    num_examples: 140
+  - name: test
+    num_bytes: 0
+    num_examples: 522
+- config_name: cs-staged
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 26036
+  - name: validation
+    num_bytes: 0
+    num_examples: 4272
+  - name: test
+    num_bytes: 0
+    num_examples: 18664
+- config_name: cv-staged
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 8888
+  - name: validation
+    num_bytes: 0
+    num_examples: 8185
+  - name: test
+    num_bytes: 0
+    num_examples: 27199
+- config_name: cs-staged-wild
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 26036
+  - name: validation
+    num_bytes: 0
+    num_examples: 4272
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: cv-staged-wild
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 8888
+  - name: validation
+    num_bytes: 0
+    num_examples: 8185
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
+- config_name: OOPS
+  features:
+  - name: path
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': walk
+          '1': fall
+          '2': fallen
+          '3': sit_down
+          '4': sitting
+          '5': lie_down
+          '6': lying
+          '7': stand_up
+          '8': standing
+          '9': other
+          '10': kneel_down
+          '11': kneeling
+          '12': squat_down
+          '13': squatting
+          '14': crawl
+          '15': jump
+  - name: start
+    dtype: float32
+  - name: end
+    dtype: float32
+  - name: subject
+    dtype: int32
+  - name: cam
+    dtype: int32
+  - name: dataset
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 0
+    num_examples: 1023
+  - name: validation
+    num_bytes: 0
+    num_examples: 482
+  - name: test
+    num_bytes: 0
+    num_examples: 3673
 ---
 [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 </br>
@@ -64,7 +2043,8 @@ Also have a look for additional information on our project page:
 
 The repository is organized as follows:
 
-- `omnifall.py` - Custom HuggingFace dataset builder (handles all configs)
+- `omnifall_builder.py` - Dataset builder (reference, not used by HF directly)
+- `parquet/` - Pre-built parquet files for all configs (used by `load_dataset`)
 - `labels/` - CSV files containing temporal segment annotations
   - Staged/OOPS labels: 7 columns (`path, label, start, end, subject, cam, dataset`)
   - OF-Syn labels: 19 columns (7 core + 12 demographic/scene metadata)
@@ -129,13 +2109,13 @@ path/to/clip
 
 ## Evaluation Protocols
 
-All configurations are defined in the `omnifall.py` dataset builder and loaded via `load_dataset("simplexsigil2/omnifall", "<config_name>")`.
+All configurations are loaded via `load_dataset("simplexsigil2/omnifall", "<config_name>")`.
 
 ### Labels (no train/val/test splits)
 - `labels` (default): All staged + OOPS labels (52k segments, 7 columns)
 - `labels-syn`: OF-Syn labels with demographic metadata (19k segments, 19 columns)
 - `metadata-syn`: OF-Syn video-level metadata (12k videos)
-- `framewise-syn`: OF-Syn frame-wise HDF5 labels (81 labels per video)
+- `framewise-syn`: OF-Syn frame-wise HDF5 labels (81 labels per video). **Requires the `omnifall` package (coming soon).**
 
 ### OF-Staged Configs
 - `of-sta-cs`: 8 staged datasets, cross-subject splits
@@ -144,7 +2124,7 @@ All configurations are defined in the `omnifall.py` dataset builder and loaded v
 ### OF-ItW Config
 - `of-itw`: OOPS-Fall in-the-wild genuine accidents
 
-OF-ItW supports optional video loading via `include_video=True` with `oops_video_dir` (see examples below). Videos are not hosted here due to licensing; run `prepare_oops_videos.py` to download them from the [original OOPS source](https://oops.cs.columbia.edu/data/).
+Video loading requires the `omnifall` package (coming soon). See examples below.
 
 ### OF-Syn Configs
 - `of-syn`: Fixed randomized 80/10/10 split
@@ -152,7 +2132,7 @@ OF-ItW supports optional video loading via `include_video=True` with `oops_video
 - `of-syn-cross-ethnicity`: Cross-ethnicity split
 - `of-syn-cross-bmi`: Cross-BMI split (train: normal/underweight, test: obese)
 
-All OF-Syn configs support optional video loading via `include_video=True` (see examples below).
+Video loading for OF-Syn configs requires the `omnifall` package (coming soon).
 
 ### Cross-Domain Evaluation
 - `of-sta-itw-cs`: Train/val on staged CS, test on OOPS
@@ -180,7 +2160,7 @@ The following old config names still work but emit a deprecation warning:
 
 ## Examples
 
-For a complete interactive walkthrough of all configs, video loading, and label visualization, see the [example notebook](test_omnifall_dataset.ipynb).
+For a complete interactive walkthrough of all configs, video loading, and label visualization, see the [example notebook](omnifall_dataset_examples.ipynb).
 
 ```python
 from datasets import load_dataset
@@ -210,57 +2190,17 @@ labels = load_dataset("simplexsigil2/omnifall", "labels")["train"]
 syn_labels = load_dataset("simplexsigil2/omnifall", "labels-syn")["train"]
 ```
 
-### Loading OF-Syn videos
+### Loading Videos
 
-OF-Syn configs support `include_video=True` to download and include the video files (~9 GB download and disk space).
-By default, videos are returned as decoded `Video()` objects. Set `decode_video=False` to get file paths instead.
+Video loading (OF-Syn, OF-ItW, and cross-domain configs) requires the `omnifall` Python package, which will be available on PyPI soon. The package handles video download, caching, and integration with HuggingFace datasets.
 
-```python
-from datasets import load_dataset
-
-# Load with decoded video (HF Video() feature)
-ds = load_dataset("simplexsigil2/omnifall", "of-syn",
-                  include_video=True, trust_remote_code=True)
-sample = ds["train"][0]
-print(sample["video"])  # VideoReader object
-
-# Load with file paths only (faster, for custom decoding)
-ds = load_dataset("simplexsigil2/omnifall", "of-syn",
-                  include_video=True, decode_video=False, trust_remote_code=True)
-sample = ds["train"][0]
-print(sample["video"])  # "/path/to/cached/fall/fall_ch_001.mp4"
-
-# Cross-domain with video: train/val (syn) and test (itw) both have videos
-ds = load_dataset("simplexsigil2/omnifall", "of-syn-itw",
-                  include_video=True, decode_video=False,
-                  oops_video_dir="/path/to/oops_prepared",
-                  trust_remote_code=True)
-print(ds["train"][0]["video"])  # syn video path (auto-downloaded)
-print(ds["test"][0]["video"])   # itw video path (from oops_video_dir)
-```
-
-### Loading OF-ItW (OOPS) videos
-
-OOPS videos are not hosted in this repository due to licensing. To load OF-ItW with videos, first prepare the OOPS videos using the included script:
+For OOPS videos specifically, you can prepare them manually using the included script:
 
 ```bash
-# Step 1: Prepare OOPS videos (~45GB streamed from source, ~2.6GB disk space)
 python prepare_oops_videos.py --output_dir /path/to/oops_prepared
 ```
 
-```python
-# Step 2: Load OF-ItW with videos
-from datasets import load_dataset
-
-ds = load_dataset("simplexsigil2/omnifall", "of-itw",
-                  include_video=True, decode_video=False,
-                  oops_video_dir="/path/to/oops_prepared",
-                  trust_remote_code=True)
-sample = ds["train"][0]
-print(sample["video"])  # "/path/to/oops_prepared/falls/BestFailsofWeek2July2016_FailArmy9.mp4"
-```
-
-The preparation script streams the full [OOPS dataset](https://oops.cs.columbia.edu/data/) archive (~45GB download) from the original source and extracts only the 818 videos used in OF-ItW. The archive is streamed and never written to disk, so only ~2.6GB of disk space is needed for the extracted videos. If you already have the OOPS archive downloaded locally, pass it with `--oops_archive /path/to/video_and_anns.tar.gz`.
+The preparation streams the full OOPS archive from the original source and extracts only the 818 videos used in OF-ItW. The archive is streamed and never written to disk, so only ~2.6GB of disk space is needed. If you already have the OOPS archive downloaded locally, pass it with `--oops_archive /path/to/video_and_anns.tar.gz`.
 
 ## Label definitions
 

generate_parquet.py

@@ -0,0 +1,428 @@
+"""One-time script to generate parquet files for all OmniFall HF configs.
+
+Created for the HF datasets 4.6 migration (dataset scripts no longer supported).
+Generates parquet files that enable native load_dataset() without custom builder code.
+Can be safely deleted after parquet files are committed to the Hub.
+
+Usage:
+    python generate_parquet.py
+"""
+
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+REPO_ROOT = Path(__file__).parent
+PARQUET_DIR = REPO_ROOT / "parquet"
+
+# ---- Label and split file paths ----
+
+STAGED_DATASETS = [
+    "caucafall", "cmdfall", "edf", "gmdcsa24",
+    "le2i", "mcfd", "occu", "up_fall",
+]
+
+# Label CSV filenames (note: GMDCSA24 has capitalized filename)
+STAGED_LABEL_FILES = {
+    "caucafall": "labels/caucafall.csv",
+    "cmdfall": "labels/cmdfall.csv",
+    "edf": "labels/edf.csv",
+    "gmdcsa24": "labels/GMDCSA24.csv",
+    "le2i": "labels/le2i.csv",
+    "mcfd": "labels/mcfd.csv",
+    "occu": "labels/occu.csv",
+    "up_fall": "labels/up_fall.csv",
+}
+ITW_LABEL_FILE = "labels/OOPS.csv"
+SYN_LABEL_FILE = "labels/of-syn.csv"
+METADATA_FILE = "videos/metadata.csv"
+
+CORE_COLUMNS = ["path", "label", "start", "end", "subject", "cam", "dataset"]
+DEMOGRAPHIC_COLUMNS = [
+    "age_group", "gender_presentation", "monk_skin_tone",
+    "race_ethnicity_omb", "bmi_band", "height_band",
+    "environment_category", "camera_shot", "speed",
+    "camera_elevation", "camera_azimuth", "camera_distance",
+]
+SYN_COLUMNS = CORE_COLUMNS + DEMOGRAPHIC_COLUMNS
+METADATA_COLUMNS = ["path", "dataset"] + DEMOGRAPHIC_COLUMNS
+
+# ---- Deprecated aliases ----
+
+DEPRECATED_ALIASES = {
+    "cs-staged": "of-sta-cs",
+    "cv-staged": "of-sta-cv",
+    "cs-staged-wild": "of-sta-itw-cs",
+    "cv-staged-wild": "of-sta-itw-cv",
+    "OOPS": "of-itw",
+}
+
+
+# ---- Helpers ----
+
+def load_csv(relpath):
+    """Load a CSV file relative to REPO_ROOT."""
+    return pd.read_csv(REPO_ROOT / relpath)
+
+
+def load_staged_labels(datasets=None):
+    """Load and concatenate staged label CSVs."""
+    if datasets is None:
+        datasets = STAGED_DATASETS
+    dfs = [load_csv(STAGED_LABEL_FILES[ds]) for ds in datasets]
+    return pd.concat(dfs, ignore_index=True)
+
+
+def load_itw_labels():
+    """Load OOPS/ItW labels."""
+    return load_csv(ITW_LABEL_FILE)
+
+
+def load_syn_labels():
+    """Load OF-Syn labels (19-col)."""
+    return load_csv(SYN_LABEL_FILE)
+
+
+def staged_split_files(split_type, split_name):
+    """Return list of split CSV relative paths for all 8 staged datasets."""
+    return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in STAGED_DATASETS]
+
+
+def merge_split_labels(split_files, labels_df):
+    """Merge split paths with labels, replicating _gen_split_merge logic."""
+    split_dfs = [load_csv(sf) for sf in split_files]
+    split_df = pd.concat(split_dfs, ignore_index=True)
+    merged = pd.merge(split_df, labels_df, on="path", how="left")
+    # Drop rows where the path didn't match any label (orphaned split entries)
+    unmatched = merged["label"].isna()
+    if unmatched.any():
+        n = unmatched.sum()
+        paths = merged.loc[unmatched, "path"].tolist()
+        print(f"  WARNING: Dropping {n} unmatched path(s): {paths}")
+        merged = merged[~unmatched].reset_index(drop=True)
+    return merged
+
+
+def cast_core_dtypes(df):
+    """Cast core columns to correct dtypes for parquet/ClassLabel."""
+    df = df.copy()
+    df["path"] = df["path"].astype(str)
+    df["label"] = df["label"].astype(int)
+    df["start"] = df["start"].astype(np.float32)
+    df["end"] = df["end"].astype(np.float32)
+    df["subject"] = df["subject"].astype(np.int32)
+    df["cam"] = df["cam"].astype(np.int32)
+    df["dataset"] = df["dataset"].astype(str)
+    return df
+
+
+def cast_demographic_dtypes(df):
+    """Cast demographic columns to string (for ClassLabel encoding)."""
+    df = df.copy()
+    for col in DEMOGRAPHIC_COLUMNS:
+        if col in df.columns:
+            df[col] = df[col].astype(str)
+    return df
+
+
+def select_and_cast(df, columns, schema="core"):
+    """Select columns and cast dtypes."""
+    df = df[columns].copy()
+    if schema in ("core", "syn"):
+        df = cast_core_dtypes(df)
+    if schema in ("syn", "metadata"):
+        df = cast_demographic_dtypes(df)
+    return df
+
+
+def write_parquet(df, config_name, split_name):
+    """Write a dataframe as a parquet file in the expected layout.
+
+    Returns the output path, or None if the dataframe is empty (Arrow can't
+    handle 0-row parquet files).
+    """
+    if len(df) == 0:
+        print(f"  SKIP {config_name}/{split_name}: 0 rows (not written)")
+        return None
+    out_dir = PARQUET_DIR / config_name
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_path = out_dir / f"{split_name}-00000-of-00001.parquet"
+    df.to_parquet(out_path, index=False)
+    return out_path
+
+
+def generate_split_config(config_name, split_type, split_files_fn, labels_df, columns,
+                          schema="core"):
+    """Generate train/val/test parquet files for a split-based config."""
+    results = {}
+    for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
+        sf = split_files_fn(split_type, csv_name)
+        merged = merge_split_labels(sf, labels_df)
+        df = select_and_cast(merged, columns, schema)
+        path = write_parquet(df, config_name, split_name)
+        results[split_name] = len(df)
+    return results
+
+
+def copy_parquet(source_config, target_config):
+    """Copy parquet files from source config to target config (for deprecated aliases)."""
+    src_dir = PARQUET_DIR / source_config
+    dst_dir = PARQUET_DIR / target_config
+    dst_dir.mkdir(parents=True, exist_ok=True)
+    results = {}
+    for src_file in sorted(src_dir.glob("*.parquet")):
+        dst_file = dst_dir / src_file.name
+        # Read and re-write to avoid symlink issues with git
+        df = pd.read_parquet(src_file)
+        df.to_parquet(dst_file, index=False)
+        split_name = src_file.stem.split("-")[0]
+        results[split_name] = len(df)
+    return results
+
+
+# ---- Config generators ----
+
+def gen_labels():
+    """Config: labels - All staged + OOPS labels, single train split."""
+    staged = load_staged_labels()
+    itw = load_itw_labels()
+    df = pd.concat([staged, itw], ignore_index=True)
+    df = select_and_cast(df, CORE_COLUMNS, "core")
+    path = write_parquet(df, "labels", "train")
+    return {"labels": {"train": len(df)}}
+
+
+def gen_labels_syn():
+    """Config: labels-syn - OF-Syn labels with demographics, single train split."""
+    df = load_syn_labels()
+    df = select_and_cast(df, SYN_COLUMNS, "syn")
+    path = write_parquet(df, "labels-syn", "train")
+    return {"labels-syn": {"train": len(df)}}
+
+
+def gen_metadata_syn():
+    """Config: metadata-syn - OF-Syn video-level metadata, single train split."""
+    df = load_csv(METADATA_FILE)
+    # Select only the metadata columns (drop prompt_id)
+    metadata_cols = ["path"] + DEMOGRAPHIC_COLUMNS
+    available = [c for c in metadata_cols if c in df.columns]
+    df = df[available].drop_duplicates(subset=["path"]).reset_index(drop=True)
+    df["dataset"] = "of-syn"
+    df = select_and_cast(df, METADATA_COLUMNS, "metadata")
+    path = write_parquet(df, "metadata-syn", "train")
+    return {"metadata-syn": {"train": len(df)}}
+
+
+def gen_of_sta(split_type):
+    """Config: of-sta-cs / of-sta-cv - 8 staged datasets combined."""
+    config_name = f"of-sta-{split_type}"
+    labels = load_staged_labels()
+    results = generate_split_config(
+        config_name, split_type,
+        lambda st, sn: staged_split_files(st, sn),
+        labels, CORE_COLUMNS, "core",
+    )
+    return {config_name: results}
+
+
+def gen_of_itw():
+    """Config: of-itw - OOPS-Fall in-the-wild."""
+    labels = load_itw_labels()
+    results = {}
+    for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
+        sf = [f"splits/cs/OOPS/{csv_name}.csv"]
+        merged = merge_split_labels(sf, labels)
+        df = select_and_cast(merged, CORE_COLUMNS, "core")
+        write_parquet(df, "of-itw", split_name)
+        results[split_name] = len(df)
+    return {"of-itw": results}
+
+
+def gen_of_syn(split_type, config_name):
+    """Config: of-syn variants."""
+    labels = load_syn_labels()
+    results = {}
+    for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
+        sf = [f"splits/syn/{split_type}/{csv_name}.csv"]
+        merged = merge_split_labels(sf, labels)
+        df = select_and_cast(merged, SYN_COLUMNS, "syn")
+        write_parquet(df, config_name, split_name)
+        results[split_name] = len(df)
+    return {config_name: results}
+
+
+def gen_crossdomain(config_name, train_split_type, train_source, test_split_type,
+                    test_source):
+    """Config: cross-domain configs (train from one source, test from another)."""
+    # Load labels for train and test sources
+    if train_source == "staged":
+        train_labels = load_staged_labels()
+        train_split_fn = lambda sn: staged_split_files(train_split_type, sn)
+    elif train_source == "syn":
+        train_labels = load_syn_labels()
+        train_split_fn = lambda sn: [f"splits/syn/{train_split_type}/{sn}.csv"]
+    else:
+        raise ValueError(f"Unknown train_source: {train_source}")
+
+    if test_source == "itw":
+        test_labels = load_itw_labels()
+        test_split_fn = lambda sn: [f"splits/{test_split_type}/OOPS/{sn}.csv"]
+    else:
+        raise ValueError(f"Unknown test_source: {test_source}")
+
+    results = {}
+
+    # Train and val come from train source
+    for split_name, csv_name in [("train", "train"), ("validation", "val")]:
+        sf = train_split_fn(csv_name)
+        merged = merge_split_labels(sf, train_labels)
+        # Cross-domain always uses core 7-col schema
+        df = select_and_cast(merged, CORE_COLUMNS, "core")
+        write_parquet(df, config_name, split_name)
+        results[split_name] = len(df)
+
+    # Test comes from test source
+    sf = test_split_fn("test")
+    merged = merge_split_labels(sf, test_labels)
+    df = select_and_cast(merged, CORE_COLUMNS, "core")
+    write_parquet(df, config_name, "test")
+    results["test"] = len(df)
+
+    return {config_name: results}
+
+
+def gen_aggregate(split_type):
+    """Config: cs / cv - all staged + OOPS combined."""
+    config_name = split_type
+    all_labels = pd.concat([load_staged_labels(), load_itw_labels()], ignore_index=True)
+    results = {}
+    for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
+        sf = staged_split_files(split_type, csv_name) + [
+            f"splits/{split_type}/OOPS/{csv_name}.csv"
+        ]
+        merged = merge_split_labels(sf, all_labels)
+        df = select_and_cast(merged, CORE_COLUMNS, "core")
+        write_parquet(df, config_name, split_name)
+        results[split_name] = len(df)
+    return {config_name: results}
+
+
+def gen_individual(ds_name):
+    """Config: individual dataset with CS splits."""
+    labels = load_csv(STAGED_LABEL_FILES[ds_name])
+    results = {}
+    for split_name, csv_name in [("train", "train"), ("validation", "val"), ("test", "test")]:
+        sf = [f"splits/cs/{ds_name}/{csv_name}.csv"]
+        merged = merge_split_labels(sf, labels)
+        df = select_and_cast(merged, CORE_COLUMNS, "core")
+        write_parquet(df, ds_name, split_name)
+        results[split_name] = len(df)
+    return {ds_name: results}
+
+
+# ---- Main ----
+
+def main():
+    print(f"Generating parquet files in: {PARQUET_DIR}")
+    PARQUET_DIR.mkdir(parents=True, exist_ok=True)
+
+    all_results = {}
+
+    # Labels configs (single train split)
+    print("\n--- Labels configs ---")
+    for gen_fn in [gen_labels, gen_labels_syn, gen_metadata_syn]:
+        result = gen_fn()
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # OF-Staged configs
+    print("\n--- OF-Staged configs ---")
+    for st in ["cs", "cv"]:
+        result = gen_of_sta(st)
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # OF-ItW config
+    print("\n--- OF-ItW config ---")
+    result = gen_of_itw()
+    all_results.update(result)
+    for config, splits in result.items():
+        for split, count in splits.items():
+            print(f"  {config}/{split}: {count} rows")
+
+    # OF-Syn configs
+    print("\n--- OF-Syn configs ---")
+    syn_configs = [
+        ("random", "of-syn"),
+        ("cross_age", "of-syn-cross-age"),
+        ("cross_ethnicity", "of-syn-cross-ethnicity"),
+        ("cross_bmi", "of-syn-cross-bmi"),
+    ]
+    for split_type, config_name in syn_configs:
+        result = gen_of_syn(split_type, config_name)
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # Cross-domain configs
+    print("\n--- Cross-domain configs ---")
+    crossdomain_configs = [
+        ("of-sta-itw-cs", "cs", "staged", "cs", "itw"),
+        ("of-sta-itw-cv", "cv", "staged", "cv", "itw"),
+        ("of-syn-itw", "random", "syn", "cs", "itw"),
+    ]
+    for config_name, train_st, train_src, test_st, test_src in crossdomain_configs:
+        result = gen_crossdomain(config_name, train_st, train_src, test_st, test_src)
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # Aggregate configs
+    print("\n--- Aggregate configs ---")
+    for st in ["cs", "cv"]:
+        result = gen_aggregate(st)
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # Individual dataset configs
+    print("\n--- Individual dataset configs ---")
+    for ds_name in STAGED_DATASETS:
+        result = gen_individual(ds_name)
+        all_results.update(result)
+        for config, splits in result.items():
+            for split, count in splits.items():
+                print(f"  {config}/{split}: {count} rows")
+
+    # Deprecated aliases (copy parquet files)
+    print("\n--- Deprecated aliases ---")
+    for old_name, new_name in DEPRECATED_ALIASES.items():
+        result = copy_parquet(new_name, old_name)
+        for split, count in result.items():
+            print(f"  {old_name}/{split}: {count} rows (alias of {new_name})")
+        all_results[old_name] = result
+
+    # Summary
+    print(f"\n{'='*60}")
+    print(f"Generated parquet files for {len(all_results)} configs")
+    total_files = sum(1 for d in PARQUET_DIR.rglob("*.parquet"))
+    print(f"Total parquet files: {total_files}")
+
+    # Print total size
+    total_bytes = sum(f.stat().st_size for f in PARQUET_DIR.rglob("*.parquet"))
+    print(f"Total size: {total_bytes / 1024 / 1024:.1f} MB")
+
+    return all_results
+
+
+if __name__ == "__main__":
+    main()

omnifall_builder.py

@@ -0,0 +1,1192 @@
+"""OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
+
+This dataset builder provides unified access to the OmniFall benchmark, which integrates:
+- OF-Staged (OF-Sta): 8 public staged fall detection datasets (~14h single-view)
+- OF-In-the-Wild (OF-ItW): Curated genuine accident videos from OOPS (~2.7h)
+- OF-Synthetic (OF-Syn): 12,000 synthetic videos generated with Wan 2.2 (~17h)
+
+All components share a 16-class activity taxonomy. Staged datasets use classes 0-9,
+while OF-ItW and OF-Syn use the full 0-15 range.
+"""
+
+import os
+import warnings
+import pandas as pd
+import datasets
+from datasets import (
+    BuilderConfig,
+    GeneratorBasedBuilder,
+    Features,
+    Value,
+    ClassLabel,
+    Sequence,
+    SplitGenerator,
+    Split,
+    Video,
+)
+
+_CITATION = """\
+@misc{omnifall,
+      title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection},
+      author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi D\\\"uger and Rainer Stiefelhagen},
+      year={2025},
+      eprint={2505.19889},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.19889},
+}
+"""
+
+_DESCRIPTION = """\
+OmniFall is a comprehensive benchmark that unifies staged, in-the-wild, and synthetic
+fall detection datasets under a common 16-class activity taxonomy.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/simplexsigil2/omnifall"
+_LICENSE = "cc-by-nc-4.0"
+
+# 16 activity classes shared across all components
+_ACTIVITY_LABELS = [
+    "walk",         # 0
+    "fall",         # 1
+    "fallen",       # 2
+    "sit_down",     # 3
+    "sitting",      # 4
+    "lie_down",     # 5
+    "lying",        # 6
+    "stand_up",     # 7
+    "standing",     # 8
+    "other",        # 9
+    "kneel_down",   # 10
+    "kneeling",     # 11
+    "squat_down",   # 12
+    "squatting",    # 13
+    "crawl",        # 14
+    "jump",         # 15
+]
+
+# Demographic and scene metadata categories (OF-Syn only)
+_AGE_GROUPS = [
+    "toddlers_1_4", "children_5_12", "teenagers_13_17",
+    "young_adults_18_34", "middle_aged_35_64", "elderly_65_plus",
+]
+_GENDERS = ["male", "female"]
+_SKIN_TONES = [f"mst{i}" for i in range(1, 11)]
+_ETHNICITIES = ["white", "black", "asian", "hispanic_latino", "aian", "nhpi", "mena"]
+_BMI_BANDS = ["underweight", "normal", "overweight", "obese"]
+_HEIGHT_BANDS = ["short", "avg", "tall"]
+_ENVIRONMENTS = ["indoor", "outdoor"]
+_CAMERA_ELEVATIONS = ["eye", "low", "high", "top"]
+_CAMERA_AZIMUTHS = ["front", "rear", "left", "right"]
+_CAMERA_DISTANCES = ["medium", "far"]
+_CAMERA_SHOTS = ["static_wide", "static_medium_wide"]
+_SPEEDS = ["24fps_rt", "25fps_rt", "30fps_rt", "std_rt"]
+
+# The 8 staged datasets
+_STAGED_DATASETS = [
+    "caucafall", "cmdfall", "edf", "gmdcsa24",
+    "le2i", "mcfd", "occu", "up_fall",
+]
+
+# Label CSV file paths (relative to repo root)
+_STAGED_LABEL_FILES = [f"labels/{name}.csv" for name in [
+    "caucafall", "cmdfall", "edf", "GMDCSA24",
+    "le2i", "mcfd", "occu", "up_fall",
+]]
+_ITW_LABEL_FILE = "labels/OOPS.csv"
+_SYN_LABEL_FILE = "labels/of-syn.csv"
+_SYN_VIDEO_ARCHIVE = "data_files/omnifall-synthetic_av1.tar"
+
+# OOPS video auto-download configuration
+_OOPS_CACHE_DIR = os.path.join(os.path.expanduser("~"), ".cache", "omnifall", "oops_prepared")
+_OOPS_URL = "https://oops.cs.columbia.edu/data/video_and_anns.tar.gz"
+_OOPS_EXPECTED_VIDEO_COUNT = 818
+_OOPS_MAPPING_FILE = "data_files/oops_video_mapping.csv"
+
+_OOPS_LICENSE_TEXT = """\
+==========================================================================
+OOPS Dataset License Notice
+==========================================================================
+
+The OF-ItW component of OmniFall uses videos from the OOPS dataset.
+The following notice is from the OOPS dataset website
+(https://oops.cs.columbia.edu/data/):
+
+  "By pressing any of the links above, you acknowledge that we do not
+   own the copyright to these videos and that they are solely provided
+   for non-commercial research and/or educational purposes. This dataset
+   is licensed under a Creative Commons Attribution-NonCommercial-
+   ShareAlike 4.0 International License."
+
+If you use OF-ItW in your research, please also cite the OOPS paper:
+
+  @inproceedings{{epstein2020oops,
+    title={{Oops! predicting unintentional action in video}},
+    author={{Epstein, Dave and Chen, Boyuan and Vondrick, Carl}},
+    booktitle={{Proceedings of the IEEE/CVF Conference on Computer
+               Vision and Pattern Recognition}},
+    pages={{919--929}},
+    year={{2020}}
+  }}
+
+The download will stream ~45GB from the OOPS website and extract {count}
+videos (~2.6GB disk space) to: {cache_dir}
+==========================================================================
+"""
+
+
+# ---- Feature schema definitions ----
+
+def _core_features():
+    """7-column schema for staged/OOPS data."""
+    return Features({
+        "path": Value("string"),
+        "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
+        "start": Value("float32"),
+        "end": Value("float32"),
+        "subject": Value("int32"),
+        "cam": Value("int32"),
+        "dataset": Value("string"),
+    })
+
+
+def _syn_features():
+    """19-column schema for synthetic data (core + demographic/scene metadata)."""
+    return Features({
+        "path": Value("string"),
+        "label": ClassLabel(num_classes=16, names=_ACTIVITY_LABELS),
+        "start": Value("float32"),
+        "end": Value("float32"),
+        "subject": Value("int32"),
+        "cam": Value("int32"),
+        "dataset": Value("string"),
+        # Demographic metadata
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        # Scene metadata
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+
+
+def _syn_metadata_features():
+    """Feature schema for OF-Syn metadata config (video-level, no temporal segments)."""
+    return Features({
+        "path": Value("string"),
+        "dataset": Value("string"),
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+
+
+def _syn_framewise_features():
+    """Feature schema for OF-Syn frame-wise labels (81 labels per video)."""
+    return Features({
+        "path": Value("string"),
+        "dataset": Value("string"),
+        "frame_labels": Sequence(
+            ClassLabel(num_classes=16, names=_ACTIVITY_LABELS), length=81
+        ),
+        "age_group": ClassLabel(num_classes=6, names=_AGE_GROUPS),
+        "gender_presentation": ClassLabel(num_classes=2, names=_GENDERS),
+        "monk_skin_tone": ClassLabel(num_classes=10, names=_SKIN_TONES),
+        "race_ethnicity_omb": ClassLabel(num_classes=7, names=_ETHNICITIES),
+        "bmi_band": ClassLabel(num_classes=4, names=_BMI_BANDS),
+        "height_band": ClassLabel(num_classes=3, names=_HEIGHT_BANDS),
+        "environment_category": ClassLabel(num_classes=2, names=_ENVIRONMENTS),
+        "camera_shot": ClassLabel(num_classes=2, names=_CAMERA_SHOTS),
+        "speed": ClassLabel(num_classes=4, names=_SPEEDS),
+        "camera_elevation": ClassLabel(num_classes=4, names=_CAMERA_ELEVATIONS),
+        "camera_azimuth": ClassLabel(num_classes=4, names=_CAMERA_AZIMUTHS),
+        "camera_distance": ClassLabel(num_classes=2, names=_CAMERA_DISTANCES),
+    })
+
+
+def _paths_only_features():
+    """Minimal feature schema for paths-only mode."""
+    return Features({"path": Value("string")})
+
+
+# ---- Config ----
+
+class OmniFallConfig(BuilderConfig):
+    """BuilderConfig for OmniFall dataset.
+
+    Args:
+        config_type: What kind of data to load.
+            "labels" - All labels in a single split (no train/val/test).
+            "split" - Train/val/test splits from split CSV files.
+            "metadata" - Video-level metadata (OF-Syn only).
+            "framewise" - Frame-wise HDF5 labels (OF-Syn only).
+        data_source: Which component(s) to load.
+            "staged" - 8 staged lab datasets
+            "itw" - OOPS in-the-wild
+            "syn" - OF-Syn synthetic
+            "staged+itw" - Staged and OOPS combined
+            Individual dataset names (e.g. "cmdfall") for single datasets.
+        split_type: Split strategy.
+            "cs" / "cv" for staged/OOPS, "random" / "cross_age" / etc. for synthetic.
+        train_source: For cross-domain configs, overrides data_source for train/val.
+        test_source: For cross-domain configs, overrides data_source for test.
+        test_split_type: For cross-domain configs, overrides split_type for test.
+        paths_only: If True, only return video paths (no label merging).
+        framewise: If True, load frame-wise labels from HDF5 (OF-Syn only).
+        include_video: If True, download and include video files.
+            For OF-Syn configs, videos are downloaded from the HF repo.
+            For OF-ItW configs, requires oops_video_dir to be set.
+        decode_video: If True (default), use Video() feature for auto-decoding.
+            If False, return absolute file path as string.
+        oops_video_dir: Path to directory containing prepared OOPS videos
+            (produced by prepare_oops_videos.py). Required when loading
+            OF-ItW configs with include_video=True.
+        deprecated_alias_for: If set, this config is a deprecated alias.
+    """
+
+    def __init__(
+        self,
+        config_type="labels",
+        data_source="staged+itw",
+        split_type=None,
+        train_source=None,
+        test_source=None,
+        test_split_type=None,
+        paths_only=False,
+        framewise=False,
+        include_video=False,
+        decode_video=True,
+        oops_video_dir=None,
+        deprecated_alias_for=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.config_type = config_type
+        self.data_source = data_source
+        self.split_type = split_type
+        self.train_source = train_source
+        self.test_source = test_source
+        self.test_split_type = test_split_type
+        self.paths_only = paths_only
+        self.framewise = framewise
+        self.include_video = include_video
+        self.decode_video = decode_video
+        self.oops_video_dir = oops_video_dir
+        self.deprecated_alias_for = deprecated_alias_for
+
+    @property
+    def is_crossdomain(self):
+        return self.train_source is not None
+
+
+def _make_config(name, description, **kwargs):
+    """Helper to create a config with consistent version."""
+    return OmniFallConfig(
+        name=name,
+        version=datasets.Version("2.0.0"),
+        description=description,
+        **kwargs,
+    )
+
+
+# ---- Config definitions ----
+
+_LABELS_CONFIGS = [
+    _make_config(
+        "labels",
+        "All staged + OOPS labels (52k segments, 7 columns). Default config.",
+        config_type="labels",
+        data_source="staged+itw",
+    ),
+    _make_config(
+        "labels-syn",
+        "OF-Syn labels with demographic metadata (19k segments, 19 columns).",
+        config_type="labels",
+        data_source="syn",
+    ),
+    _make_config(
+        "metadata-syn",
+        "OF-Syn video-level metadata (12k videos, no temporal segments).",
+        config_type="metadata",
+        data_source="syn",
+    ),
+    _make_config(
+        "framewise-syn",
+        "OF-Syn frame-wise labels from HDF5 (81 labels per video).",
+        config_type="framewise",
+        data_source="syn",
+        framewise=True,
+    ),
+]
+
+_AGGREGATE_CONFIGS = [
+    _make_config(
+        "cs",
+        "Cross-subject splits for all staged + OOPS datasets combined.",
+        config_type="split",
+        data_source="staged+itw",
+        split_type="cs",
+    ),
+    _make_config(
+        "cv",
+        "Cross-view splits for all staged + OOPS datasets combined.",
+        config_type="split",
+        data_source="staged+itw",
+        split_type="cv",
+    ),
+]
+
+_PRIMARY_CONFIGS = [
+    _make_config(
+        "of-sta-cs",
+        "OF-Staged: 8 staged datasets, cross-subject splits.",
+        config_type="split",
+        data_source="staged",
+        split_type="cs",
+    ),
+    _make_config(
+        "of-sta-cv",
+        "OF-Staged: 8 staged datasets, cross-view splits.",
+        config_type="split",
+        data_source="staged",
+        split_type="cv",
+    ),
+    _make_config(
+        "of-itw",
+        "OF-ItW: OOPS-Fall in-the-wild genuine accidents.",
+        config_type="split",
+        data_source="itw",
+        split_type="cs",
+    ),
+    _make_config(
+        "of-syn",
+        "OF-Syn: synthetic, random 80/10/10 split.",
+        config_type="split",
+        data_source="syn",
+        split_type="random",
+    ),
+    _make_config(
+        "of-syn-cross-age",
+        "OF-Syn: cross-age split (train: adults, test: children/elderly).",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_age",
+    ),
+    _make_config(
+        "of-syn-cross-ethnicity",
+        "OF-Syn: cross-ethnicity split.",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_ethnicity",
+    ),
+    _make_config(
+        "of-syn-cross-bmi",
+        "OF-Syn: cross-BMI split (train: normal/underweight, test: obese).",
+        config_type="split",
+        data_source="syn",
+        split_type="cross_bmi",
+    ),
+]
+
+_CROSSDOMAIN_CONFIGS = [
+    _make_config(
+        "of-sta-itw-cs",
+        "Cross-domain: train/val on staged CS, test on OOPS.",
+        config_type="split",
+        data_source="staged",
+        split_type="cs",
+        train_source="staged",
+        test_source="itw",
+        test_split_type="cs",
+    ),
+    _make_config(
+        "of-sta-itw-cv",
+        "Cross-domain: train/val on staged CV, test on OOPS.",
+        config_type="split",
+        data_source="staged",
+        split_type="cv",
+        train_source="staged",
+        test_source="itw",
+        test_split_type="cv",
+    ),
+    _make_config(
+        "of-syn-itw",
+        "Cross-domain: train/val on OF-Syn random, test on OOPS.",
+        config_type="split",
+        data_source="syn",
+        split_type="random",
+        train_source="syn",
+        test_source="itw",
+        test_split_type="cs",
+    ),
+]
+
+_INDIVIDUAL_CONFIGS = [
+    _make_config(
+        name,
+        f"{name} dataset with cross-subject splits.",
+        config_type="split",
+        data_source=name,
+        split_type="cs",
+    )
+    for name in _STAGED_DATASETS
+]
+
+# Deprecated aliases: defined with full correct attributes so _info() works
+# immediately (HF calls _info() during __init__, before any custom init code).
+_DEPRECATED_ALIASES = {
+    "cs-staged": "of-sta-cs",
+    "cv-staged": "of-sta-cv",
+    "cs-staged-wild": "of-sta-itw-cs",
+    "cv-staged-wild": "of-sta-itw-cv",
+    "OOPS": "of-itw",
+}
+
+# Build a lookup from config name to config object
+_ALL_NAMED_CONFIGS = {
+    cfg.name: cfg
+    for cfg in (
+        _LABELS_CONFIGS + _AGGREGATE_CONFIGS + _PRIMARY_CONFIGS
+        + _CROSSDOMAIN_CONFIGS + _INDIVIDUAL_CONFIGS
+    )
+}
+
+_DEPRECATED_CONFIGS = []
+for _old_name, _new_name in _DEPRECATED_ALIASES.items():
+    _target = _ALL_NAMED_CONFIGS[_new_name]
+    _DEPRECATED_CONFIGS.append(
+        _make_config(
+            _old_name,
+            f"DEPRECATED: Use '{_new_name}' instead.",
+            config_type=_target.config_type,
+            data_source=_target.data_source,
+            split_type=_target.split_type,
+            train_source=_target.train_source,
+            test_source=_target.test_source,
+            test_split_type=_target.test_split_type,
+            paths_only=_target.paths_only,
+            framewise=_target.framewise,
+            include_video=_target.include_video,
+            decode_video=_target.decode_video,
+            oops_video_dir=_target.oops_video_dir,
+            deprecated_alias_for=_new_name,
+        )
+    )
+
+
+# ---- Builder ----
+
+class OmniFall(GeneratorBasedBuilder):
+    """OmniFall unified fall detection benchmark builder."""
+
+    VERSION = datasets.Version("2.0.0")
+    BUILDER_CONFIG_CLASS = OmniFallConfig
+
+    BUILDER_CONFIGS = (
+        _LABELS_CONFIGS
+        + _AGGREGATE_CONFIGS
+        + _PRIMARY_CONFIGS
+        + _CROSSDOMAIN_CONFIGS
+        + _INDIVIDUAL_CONFIGS
+        + _DEPRECATED_CONFIGS
+    )
+
+    DEFAULT_CONFIG_NAME = "labels"
+
+    def _info(self):
+        """Return dataset metadata and feature schema."""
+        cfg = self.config
+
+        if cfg.config_type == "metadata":
+            features = _syn_metadata_features()
+        elif cfg.framewise:
+            features = _syn_framewise_features()
+        elif cfg.paths_only:
+            features = _paths_only_features()
+        elif cfg.is_crossdomain:
+            # Cross-domain configs mix sources, use common 7-col schema
+            features = _core_features()
+        elif cfg.data_source == "syn":
+            features = _syn_features()
+        else:
+            features = _core_features()
+
+        if cfg.include_video:
+            features["video"] = Video() if cfg.decode_video else Value("string")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    # ---- Split generators ----
+
+    def _split_generators(self, dl_manager):
+        cfg = self.config
+
+        # Emit deprecation warning
+        if cfg.deprecated_alias_for:
+            warnings.warn(
+                f"Config '{cfg.name}' is deprecated. "
+                f"Use '{cfg.deprecated_alias_for}' instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        # Labels configs: all data in a single "train" split
+        if cfg.config_type == "labels":
+            return self._labels_splits(cfg, dl_manager)
+
+        # Metadata config
+        if cfg.config_type == "metadata":
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "metadata", "metadata_path": metadata_path},
+                ),
+            ]
+
+        # Framewise config (no split, all data)
+        if cfg.config_type == "framewise":
+            archive_path = dl_manager.download_and_extract(
+                "data_files/syn_frame_wise_labels.tar.zst"
+            )
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": metadata_path,
+                        "split_file": None,
+                    },
+                ),
+            ]
+
+        # Split configs (train/val/test)
+        if cfg.config_type == "split":
+            return self._split_config_generators(cfg, dl_manager)
+
+        raise ValueError(f"Unknown config_type: {cfg.config_type}")
+
+    def _labels_splits(self, cfg, dl_manager):
+        """Generate split generators for labels-type configs."""
+        if cfg.data_source == "syn":
+            filepath = dl_manager.download(_SYN_LABEL_FILE)
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "csv_direct", "filepath": filepath},
+                ),
+            ]
+        elif cfg.data_source == "staged+itw":
+            filepaths = dl_manager.download(_STAGED_LABEL_FILES + [_ITW_LABEL_FILE])
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "csv_multi", "filepaths": filepaths},
+                ),
+            ]
+        else:
+            raise ValueError(f"Unsupported data_source for labels: {cfg.data_source}")
+
+    def _split_config_generators(self, cfg, dl_manager):
+        """Generate split generators for train/val/test split configs."""
+        if cfg.is_crossdomain:
+            return self._crossdomain_splits(cfg, dl_manager)
+
+        if cfg.data_source == "syn":
+            return self._syn_splits(cfg, dl_manager)
+        elif cfg.data_source == "staged":
+            return self._staged_splits(cfg, dl_manager)
+        elif cfg.data_source == "itw":
+            return self._itw_splits(cfg, dl_manager)
+        elif cfg.data_source == "staged+itw":
+            return self._aggregate_splits(cfg, dl_manager)
+        elif cfg.data_source in _STAGED_DATASETS:
+            return self._individual_splits(cfg, dl_manager)
+        else:
+            raise ValueError(f"Unknown data_source: {cfg.data_source}")
+
+    def _staged_split_files(self, split_type, split_name):
+        """Return list of split CSV paths for all 8 staged datasets."""
+        return [f"splits/{split_type}/{ds}/{split_name}.csv" for ds in _STAGED_DATASETS]
+
+    def _resolve_oops_video_dir(self, cfg, dl_manager):
+        """Resolve the OOPS video directory for OF-ItW configs.
+
+        Priority:
+        1. If include_video is False, return None.
+        2. If oops_video_dir is explicitly provided, validate and return it.
+        3. If cache exists with expected video count, return cache path.
+        4. Otherwise, prompt for license consent and auto-download.
+        """
+        if not cfg.include_video:
+            return None
+
+        # User explicitly provided a directory
+        if cfg.oops_video_dir:
+            video_dir = os.path.abspath(cfg.oops_video_dir)
+            if not os.path.isdir(video_dir):
+                raise FileNotFoundError(
+                    f"oops_video_dir does not exist: {video_dir}\n"
+                    "Run prepare_oops_videos.py to prepare OOPS videos first."
+                )
+            return video_dir
+
+        # Check cache
+        cache_dir = _OOPS_CACHE_DIR
+        if self._oops_cache_is_valid(cache_dir):
+            return cache_dir
+
+        # Auto-download: prompt for consent and extract
+        return self._auto_prepare_oops(cache_dir, dl_manager)
+
+    def _oops_cache_is_valid(self, cache_dir):
+        """Check if the OOPS video cache contains the expected number of videos."""
+        falls_dir = os.path.join(cache_dir, "falls")
+        if not os.path.isdir(falls_dir):
+            return False
+        mp4_count = sum(1 for f in os.listdir(falls_dir) if f.endswith(".mp4"))
+        if mp4_count >= _OOPS_EXPECTED_VIDEO_COUNT:
+            return True
+        if mp4_count > 0:
+            warnings.warn(
+                f"OOPS cache at {cache_dir} contains {mp4_count}/{_OOPS_EXPECTED_VIDEO_COUNT} "
+                f"videos (incomplete). Will re-download."
+            )
+        return False
+
+    def _auto_prepare_oops(self, cache_dir, dl_manager):
+        """Download and prepare OOPS videos with interactive license consent."""
+        import csv
+        import subprocess
+        import tarfile
+
+        # Print license and get consent
+        print(_OOPS_LICENSE_TEXT.format(
+            count=_OOPS_EXPECTED_VIDEO_COUNT, cache_dir=cache_dir,
+        ))
+        try:
+            response = input('Type "YES" to accept the license and begin download: ')
+        except EOFError:
+            raise RuntimeError(
+                "Cannot prompt for OOPS license consent in non-interactive mode.\n"
+                "Either run prepare_oops_videos.py manually and pass oops_video_dir,\n"
+                "or run this script in an interactive terminal."
+            )
+
+        if response.strip() != "YES":
+            raise RuntimeError(
+                "OOPS license not accepted. To load OF-ItW with videos, either:\n"
+                "1. Run again and type YES when prompted, or\n"
+                "2. Run prepare_oops_videos.py manually and pass oops_video_dir."
+            )
+
+        # Download the mapping file from the HF repo
+        mapping_path = dl_manager.download(_OOPS_MAPPING_FILE)
+        mapping = {}
+        with open(mapping_path) as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                mapping[row["oops_path"]] = row["itw_path"]
+
+        # Create output directory
+        os.makedirs(os.path.join(cache_dir, "falls"), exist_ok=True)
+
+        # Extract videos
+        found = self._extract_oops_videos(_OOPS_URL, mapping, cache_dir)
+
+        if found == 0:
+            raise RuntimeError(
+                "Failed to extract any OOPS videos. Check network connectivity "
+                "and try again, or use prepare_oops_videos.py with a local archive."
+            )
+
+        if found < _OOPS_EXPECTED_VIDEO_COUNT:
+            warnings.warn(
+                f"Only extracted {found}/{_OOPS_EXPECTED_VIDEO_COUNT} OOPS videos. "
+                f"Some videos may be missing from the archive."
+            )
+
+        return cache_dir
+
+    def _extract_oops_videos(self, source, mapping, output_dir):
+        """Stream through the OOPS archive and extract matching videos."""
+        import subprocess
+        import tarfile
+
+        total = len(mapping)
+        print(f"Extracting {total} videos from OOPS archive...")
+        print("(Streaming ~45GB from web, no local disk space needed for archive)")
+        print("(This may take 30-60 minutes depending on connection speed)")
+
+        os.makedirs(os.path.join(output_dir, "falls"), exist_ok=True)
+
+        found = 0
+        remaining = set(mapping.keys())
+
+        cmd = f'curl -sL "{source}" | tar -xzf - --to-stdout "oops_dataset/video.tar.gz"'
+        proc = subprocess.Popen(
+            cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+        )
+
+        try:
+            with tarfile.open(fileobj=proc.stdout, mode="r|gz") as tar:
+                for member in tar:
+                    if not remaining:
+                        break
+                    if member.name in remaining:
+                        itw_path = mapping[member.name]
+                        out_path = os.path.join(output_dir, itw_path)
+
+                        f = tar.extractfile(member)
+                        if f is not None:
+                            with open(out_path, "wb") as out_f:
+                                while True:
+                                    chunk = f.read(1024 * 1024)
+                                    if not chunk:
+                                        break
+                                    out_f.write(chunk)
+                            f.close()
+                            found += 1
+                            remaining.discard(member.name)
+                            if found % 50 == 0:
+                                print(f"  Extracted {found}/{total} videos...")
+        finally:
+            proc.stdout.close()
+            proc.wait()
+
+        print(f"Extracted {found}/{total} videos to {output_dir}")
+        if remaining:
+            print(f"WARNING: {len(remaining)} videos not found in archive.")
+
+        return found
+
+    def _make_split_merge_generators(self, split_files_per_split, label_files,
+                                     dl_manager, video_dir=None):
+        """Helper to create train/val/test SplitGenerators for split_merge mode.
+
+        Args:
+            split_files_per_split: dict mapping split name to list of relative paths.
+            label_files: list of relative label file paths.
+            dl_manager: download manager for resolving paths.
+            video_dir: path to extracted video directory, or None.
+        """
+        resolved_labels = dl_manager.download(label_files)
+        return [
+            SplitGenerator(
+                name=split_enum,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(split_files_per_split[csv_name]),
+                    "label_files": resolved_labels,
+                    "video_dir": video_dir,
+                },
+            )
+            for split_enum, csv_name in [
+                (Split.TRAIN, "train"),
+                (Split.VALIDATION, "val"),
+                (Split.TEST, "test"),
+            ]
+        ]
+
+    def _staged_splits(self, cfg, dl_manager):
+        """OF-Staged: 8 datasets combined with CS or CV splits."""
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            {sn: self._staged_split_files(st, sn) for sn in ("train", "val", "test")},
+            _STAGED_LABEL_FILES,
+            dl_manager,
+        )
+
+    def _itw_splits(self, cfg, dl_manager):
+        """OF-ItW: OOPS-Fall (CS=CV identical)."""
+        st = cfg.split_type
+        video_dir = self._resolve_oops_video_dir(cfg, dl_manager)
+        return self._make_split_merge_generators(
+            {sn: [f"splits/{st}/OOPS/{sn}.csv"] for sn in ("train", "val", "test")},
+            [_ITW_LABEL_FILE],
+            dl_manager,
+            video_dir=video_dir,
+        )
+
+    def _aggregate_splits(self, cfg, dl_manager):
+        """All staged + OOPS combined (cs or cv)."""
+        st = cfg.split_type
+        all_labels = _STAGED_LABEL_FILES + [_ITW_LABEL_FILE]
+        return self._make_split_merge_generators(
+            {sn: self._staged_split_files(st, sn) + [f"splits/{st}/OOPS/{sn}.csv"]
+             for sn in ("train", "val", "test")},
+            all_labels,
+            dl_manager,
+        )
+
+    def _individual_splits(self, cfg, dl_manager):
+        """Individual dataset with CS splits."""
+        ds_name = cfg.data_source
+        label_file_map = {
+            "caucafall": "labels/caucafall.csv",
+            "cmdfall": "labels/cmdfall.csv",
+            "edf": "labels/edf.csv",
+            "gmdcsa24": "labels/GMDCSA24.csv",
+            "le2i": "labels/le2i.csv",
+            "mcfd": "labels/mcfd.csv",
+            "occu": "labels/occu.csv",
+            "up_fall": "labels/up_fall.csv",
+        }
+        label_file = label_file_map[ds_name]
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            {sn: [f"splits/{st}/{ds_name}/{sn}.csv"] for sn in ("train", "val", "test")},
+            [label_file],
+            dl_manager,
+        )
+
+    def _syn_splits(self, cfg, dl_manager):
+        """OF-Syn split strategies."""
+        st = cfg.split_type
+        split_dir = f"splits/syn/{st}"
+
+        # Download video archive if requested
+        video_dir = None
+        if cfg.include_video:
+            video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
+
+        if cfg.framewise:
+            archive_path = dl_manager.download_and_extract(
+                "data_files/syn_frame_wise_labels.tar.zst"
+            )
+            metadata_path = dl_manager.download("videos/metadata.csv")
+            split_files = dl_manager.download(
+                {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
+            )
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": metadata_path,
+                        "split_file": split_files[csv_name],
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+
+        if cfg.paths_only:
+            split_files = dl_manager.download(
+                {sn: f"{split_dir}/{sn}.csv" for sn in ("train", "val", "test")}
+            )
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "paths_only",
+                        "split_file": split_files[csv_name],
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+
+        return self._make_split_merge_generators(
+            {sn: [f"{split_dir}/{sn}.csv"] for sn in ("train", "val", "test")},
+            [_SYN_LABEL_FILE],
+            dl_manager,
+            video_dir=video_dir,
+        )
+
+    def _crossdomain_splits(self, cfg, dl_manager):
+        """Cross-domain configs: train/val from one source, test from another."""
+        train_st = cfg.split_type
+        test_st = cfg.test_split_type or "cs"
+
+        # Resolve video directories for each source
+        train_video_dir = None
+        if cfg.include_video and cfg.train_source == "syn":
+            train_video_dir = dl_manager.download_and_extract(_SYN_VIDEO_ARCHIVE)
+
+        test_video_dir = None
+        if cfg.include_video and cfg.test_source == "itw":
+            test_video_dir = self._resolve_oops_video_dir(cfg, dl_manager)
+
+        # Determine train/val files and labels
+        if cfg.train_source == "staged":
+            train_split_files = {
+                sn: self._staged_split_files(train_st, sn)
+                for sn in ("train", "val")
+            }
+            train_labels = _STAGED_LABEL_FILES
+        elif cfg.train_source == "syn":
+            train_split_files = {
+                sn: [f"splits/syn/{train_st}/{sn}.csv"]
+                for sn in ("train", "val")
+            }
+            train_labels = [_SYN_LABEL_FILE]
+        else:
+            raise ValueError(f"Unsupported train_source: {cfg.train_source}")
+
+        # Determine test files and labels
+        if cfg.test_source == "itw":
+            test_split_files = [f"splits/{test_st}/OOPS/test.csv"]
+            test_labels = [_ITW_LABEL_FILE]
+        else:
+            raise ValueError(f"Unsupported test_source: {cfg.test_source}")
+
+        # Download all paths
+        resolved_train_labels = dl_manager.download(train_labels)
+        resolved_test_labels = dl_manager.download(test_labels)
+        resolved_test_splits = dl_manager.download(test_split_files)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(train_split_files["train"]),
+                    "label_files": resolved_train_labels,
+                    "video_dir": train_video_dir,
+                },
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": dl_manager.download(train_split_files["val"]),
+                    "label_files": resolved_train_labels,
+                    "video_dir": train_video_dir,
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": resolved_test_splits,
+                    "label_files": resolved_test_labels,
+                    "video_dir": test_video_dir,
+                },
+            ),
+        ]
+
+    # ---- Example generators ----
+
+    def _generate_examples(self, mode, **kwargs):
+        """Dispatch to the appropriate generator based on mode."""
+        if mode == "csv_direct":
+            yield from self._gen_csv_direct(**kwargs)
+        elif mode == "csv_multi":
+            yield from self._gen_csv_multi(**kwargs)
+        elif mode == "split_merge":
+            yield from self._gen_split_merge(**kwargs)
+        elif mode == "metadata":
+            yield from self._gen_metadata(**kwargs)
+        elif mode == "framewise":
+            yield from self._gen_framewise(**kwargs)
+        elif mode == "paths_only":
+            yield from self._gen_paths_only(**kwargs)
+        else:
+            raise ValueError(f"Unknown generation mode: {mode}")
+
+    def _gen_csv_direct(self, filepath):
+        """Load a single CSV file directly."""
+        df = pd.read_csv(filepath)
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+
+    def _gen_csv_multi(self, filepaths):
+        """Load and concatenate multiple CSV files."""
+        dfs = [pd.read_csv(fp) for fp in filepaths]
+        df = pd.concat(dfs, ignore_index=True)
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+
+    def _gen_split_merge(self, split_files, label_files, video_dir=None):
+        """Load split paths, merge with labels, yield examples."""
+        split_dfs = [pd.read_csv(sf) for sf in split_files]
+        split_df = pd.concat(split_dfs, ignore_index=True)
+
+        if self.config.paths_only:
+            for idx, row in split_df.iterrows():
+                yield idx, {"path": row["path"]}
+            return
+
+        label_dfs = [pd.read_csv(lf) for lf in label_files]
+        labels_df = pd.concat(label_dfs, ignore_index=True)
+
+        merged_df = pd.merge(split_df, labels_df, on="path", how="left")
+
+        for idx, row in merged_df.iterrows():
+            example = self._row_to_example(row)
+            if video_dir is not None:
+                example["video"] = os.path.join(video_dir, row["path"] + ".mp4")
+            yield idx, example
+
+    def _gen_metadata(self, metadata_path):
+        """Load OF-Syn video-level metadata."""
+        df = pd.read_csv(metadata_path)
+        metadata_cols = [
+            "path", "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]
+        available_cols = [c for c in metadata_cols if c in df.columns]
+        df = df[available_cols].drop_duplicates(subset=["path"]).reset_index(drop=True)
+        df["dataset"] = "of-syn"
+
+        for idx, row in df.iterrows():
+            yield idx, self._row_to_example(row)
+
+    def _gen_framewise(self, hdf5_dir, metadata_path, split_file=None):
+        """Load frame-wise labels from HDF5 files with metadata."""
+        import h5py
+        import tarfile
+        from pathlib import Path
+
+        metadata_df = pd.read_csv(metadata_path)
+
+        valid_paths = None
+        if split_file is not None:
+            split_df = pd.read_csv(split_file)
+            valid_paths = set(split_df["path"].tolist())
+
+        hdf5_path = Path(hdf5_dir)
+        metadata_fields = [
+            "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]
+
+        if hdf5_path.is_file() and (
+            hdf5_path.suffix == ".tar" or tarfile.is_tarfile(str(hdf5_path))
+        ):
+            idx = 0
+            with tarfile.open(hdf5_path, "r") as tar:
+                for member in tar.getmembers():
+                    if not member.name.endswith(".h5"):
+                        continue
+                    video_path = member.name.lstrip("./").replace(".h5", "")
+                    if valid_paths is not None and video_path not in valid_paths:
+                        continue
+                    try:
+                        h5_file = tar.extractfile(member)
+                        if h5_file is None:
+                            continue
+                        import tempfile
+                        with tempfile.NamedTemporaryFile(suffix=".h5", delete=True) as tmp:
+                            tmp.write(h5_file.read())
+                            tmp.flush()
+                            with h5py.File(tmp.name, "r") as f:
+                                frame_labels = f["label_indices"][:].tolist()
+                        video_metadata = metadata_df[metadata_df["path"] == video_path]
+                        if len(video_metadata) == 0:
+                            continue
+                        video_meta = video_metadata.iloc[0]
+                        example = {
+                            "path": video_path,
+                            "dataset": "of-syn",
+                            "frame_labels": frame_labels,
+                        }
+                        for field in metadata_fields:
+                            if field in video_meta and pd.notna(video_meta[field]):
+                                example[field] = str(video_meta[field])
+                        yield idx, example
+                        idx += 1
+                    except Exception as e:
+                        warnings.warn(f"Failed to process {member.name}: {e}")
+                        continue
+        else:
+            hdf5_files = sorted(hdf5_path.glob("**/*.h5"))
+            idx = 0
+            for h5_file_path in hdf5_files:
+                relative_path = h5_file_path.relative_to(hdf5_path)
+                video_path = str(relative_path.with_suffix(""))
+                if valid_paths is not None and video_path not in valid_paths:
+                    continue
+                try:
+                    with h5py.File(h5_file_path, "r") as f:
+                        frame_labels = f["label_indices"][:].tolist()
+                    video_metadata = metadata_df[metadata_df["path"] == video_path]
+                    if len(video_metadata) == 0:
+                        continue
+                    video_meta = video_metadata.iloc[0]
+                    example = {
+                        "path": video_path,
+                        "dataset": "of-syn",
+                        "frame_labels": frame_labels,
+                    }
+                    for field in metadata_fields:
+                        if field in video_meta and pd.notna(video_meta[field]):
+                            example[field] = str(video_meta[field])
+                    yield idx, example
+                    idx += 1
+                except Exception as e:
+                    warnings.warn(f"Failed to process {h5_file_path}: {e}")
+                    continue
+
+    def _gen_paths_only(self, split_file):
+        """Load paths only from a split file."""
+        df = pd.read_csv(split_file)
+        for idx, row in df.iterrows():
+            yield idx, {"path": row["path"]}
+
+    def _row_to_example(self, row):
+        """Convert a DataFrame row to a typed example dict.
+
+        Only includes fields present in the row. HuggingFace's Features.encode_example()
+        will ignore extra fields and fill missing optional fields.
+        """
+        example = {"path": str(row["path"])}
+
+        # Core temporal fields
+        for field, dtype in [
+            ("label", int), ("start", float), ("end", float),
+            ("subject", int), ("cam", int),
+        ]:
+            if field in row.index and pd.notna(row[field]):
+                example[field] = dtype(row[field])
+
+        if "dataset" in row.index and pd.notna(row["dataset"]):
+            example["dataset"] = str(row["dataset"])
+
+        # Demographic and scene metadata (present only for syn data)
+        for field in [
+            "age_group", "gender_presentation", "monk_skin_tone",
+            "race_ethnicity_omb", "bmi_band", "height_band",
+            "environment_category", "camera_shot", "speed",
+            "camera_elevation", "camera_azimuth", "camera_distance",
+        ]:
+            if field in row.index and pd.notna(row[field]):
+                example[field] = str(row[field])
+
+        return example

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