Changes

.gitignore

@@ -1 +1,4 @@
 convert_oops_via_to_csv.py
+# Symlink for local testing (HF derives dataset name from directory name)
+hf.py
+__pycache__

README.md

@@ -6,257 +6,12 @@ language:
 - en
 tags:
 - medical
+- fall-detection
+- activity-recognition
+- synthetic
 pretty_name: 'OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection'
 size_categories:
 - 10K<n<100K
-
-configs:
-  - config_name: labels
-    data_files:
-      - labels/GMDCSA24.csv
-      - labels/OOPS.csv
-      - labels/caucafall.csv
-      - labels/cmdfall.csv
-      - labels/edf.csv
-      - labels/le2i.csv
-      - labels/mcfd.csv
-      - labels/occu.csv
-      - labels/up_fall.csv
-    default: true
-    description: "Labels for all videos, load splits to get train val test paths."
-
-  # Cross-subject (cs) and Cross-validation (cv) base configurations
-  - config_name: cv
-    data_files:
-      - split: train
-        path: "splits/cv/*/train.csv"
-      - split: validation
-        path: "splits/cv/*/val.csv"
-      - split: test
-        path: "splits/cv/*/test.csv"
-    description: "Cross-validation split configuration for all datasets"
-
-  - config_name: cs
-    data_files:
-      - split: train
-        path: "splits/cs/*/train.csv"
-      - split: validation
-        path: "splits/cs/*/val.csv"
-      - split: test
-        path: "splits/cs/*/test.csv"
-    description: "Cross-subject split configuration for all datasets"
-
-  # New multi-dataset splits for staged/wild settings
-  - config_name: cs-staged
-    data_files:
-      - split: train
-        path:
-          - "splits/cs/caucafall/train.csv"
-          - "splits/cs/cmdfall/train.csv"
-          - "splits/cs/edf/train.csv"
-          - "splits/cs/gmdcsa24/train.csv"
-          - "splits/cs/le2i/train.csv"
-          - "splits/cs/mcfd/train.csv"
-          - "splits/cs/occu/train.csv"
-          - "splits/cs/up_fall/train.csv"
-      - split: validation
-        path:
-          - "splits/cs/caucafall/val.csv"
-          - "splits/cs/cmdfall/val.csv"
-          - "splits/cs/edf/val.csv"
-          - "splits/cs/gmdcsa24/val.csv"
-          - "splits/cs/le2i/val.csv"
-          - "splits/cs/mcfd/val.csv"
-          - "splits/cs/occu/val.csv"
-          - "splits/cs/up_fall/val.csv"
-      - split: test
-        path:
-          - "splits/cs/caucafall/test.csv"
-          - "splits/cs/cmdfall/test.csv"
-          - "splits/cs/edf/test.csv"
-          - "splits/cs/gmdcsa24/test.csv"
-          - "splits/cs/le2i/test.csv"
-          - "splits/cs/mcfd/test.csv"
-          - "splits/cs/occu/test.csv"
-          - "splits/cs/up_fall/test.csv"
-    description: "Cross-subject splits on staged datasets only (excluding OOPS)"
-
-  - config_name: cv-staged
-    data_files:
-      - split: train
-        path:
-          - "splits/cv/caucafall/train.csv"
-          - "splits/cv/cmdfall/train.csv"
-          - "splits/cv/edf/train.csv"
-          - "splits/cv/gmdcsa24/train.csv"
-          - "splits/cv/le2i/train.csv"
-          - "splits/cv/mcfd/train.csv"
-          - "splits/cv/occu/train.csv"
-          - "splits/cv/up_fall/train.csv"
-      - split: validation
-        path:
-          - "splits/cv/caucafall/val.csv"
-          - "splits/cv/cmdfall/val.csv"
-          - "splits/cv/edf/val.csv"
-          - "splits/cv/gmdcsa24/val.csv"
-          - "splits/cv/le2i/val.csv"
-          - "splits/cv/mcfd/val.csv"
-          - "splits/cv/occu/val.csv"
-          - "splits/cv/up_fall/val.csv"
-      - split: test
-        path:
-          - "splits/cv/caucafall/test.csv"
-          - "splits/cv/cmdfall/test.csv"
-          - "splits/cv/edf/test.csv"
-          - "splits/cv/gmdcsa24/test.csv"
-          - "splits/cv/le2i/test.csv"
-          - "splits/cv/mcfd/test.csv"
-          - "splits/cv/occu/test.csv"
-          - "splits/cv/up_fall/test.csv"
-    description: "Cross-validation splits on staged datasets only (excluding OOPS)"
-
-  - config_name: cs-staged-wild
-    data_files:
-      - split: train
-        path:
-          - "splits/cs/caucafall/train.csv"
-          - "splits/cs/cmdfall/train.csv"
-          - "splits/cs/edf/train.csv"
-          - "splits/cs/gmdcsa24/train.csv"
-          - "splits/cs/le2i/train.csv"
-          - "splits/cs/mcfd/train.csv"
-          - "splits/cs/occu/train.csv"
-          - "splits/cs/up_fall/train.csv"
-      - split: validation
-        path:
-          - "splits/cs/caucafall/val.csv"
-          - "splits/cs/cmdfall/val.csv"
-          - "splits/cs/edf/val.csv"
-          - "splits/cs/gmdcsa24/val.csv"
-          - "splits/cs/le2i/val.csv"
-          - "splits/cs/mcfd/val.csv"
-          - "splits/cs/occu/val.csv"
-          - "splits/cs/up_fall/val.csv"
-      - split: test
-        path: "splits/cs/OOPS/test.csv"
-    description: "Train and validate on staged datasets, test on wild (OOPS) dataset with cross-subject splits"
-
-  - config_name: cv-staged-wild
-    data_files:
-      - split: train
-        path:
-          - "splits/cv/caucafall/train.csv"
-          - "splits/cv/cmdfall/train.csv"
-          - "splits/cv/edf/train.csv"
-          - "splits/cv/gmdcsa24/train.csv"
-          - "splits/cv/le2i/train.csv"
-          - "splits/cv/mcfd/train.csv"
-          - "splits/cv/occu/train.csv"
-          - "splits/cv/up_fall/train.csv"
-      - split: validation
-        path:
-          - "splits/cv/caucafall/val.csv"
-          - "splits/cv/cmdfall/val.csv"
-          - "splits/cv/edf/val.csv"
-          - "splits/cv/gmdcsa24/val.csv"
-          - "splits/cv/le2i/val.csv"
-          - "splits/cv/mcfd/val.csv"
-          - "splits/cv/occu/val.csv"
-          - "splits/cv/up_fall/val.csv"
-      - split: test
-        path: "splits/cv/OOPS/test.csv"
-    description: "Train and validate on staged datasets, test on wild (OOPS) dataset with cross-validation splits"
-
-  # Individual dataset configurations
-  - config_name: OOPS
-    data_files:
-      - split: train
-        path: "splits/cs/OOPS/train.csv"
-      - split: validation
-        path: "splits/cs/OOPS/val.csv"
-      - split: test
-        path: "splits/cs/OOPS/test.csv"
-    description: "OOPS dataset with cross-subject splits"
-  
-  - config_name: caucafall
-    data_files:
-      - split: train
-        path: "splits/cs/caucafall/train.csv"
-      - split: validation
-        path: "splits/cs/caucafall/val.csv"
-      - split: test
-        path: "splits/cs/caucafall/test.csv"
-    description: "CaucaFall dataset with cross-subject splits"
-  
-  - config_name: cmdfall
-    data_files:
-      - split: train
-        path: "splits/cs/cmdfall/train.csv"
-      - split: validation
-        path: "splits/cs/cmdfall/val.csv"
-      - split: test
-        path: "splits/cs/cmdfall/test.csv"
-    description: "CMDFall dataset with cross-subject splits"
-  
-  - config_name: edf
-    data_files:
-      - split: train
-        path: "splits/cs/edf/train.csv"
-      - split: validation
-        path: "splits/cs/edf/val.csv"
-      - split: test
-        path: "splits/cs/edf/test.csv"
-    description: "EDF dataset with cross-subject splits"
-  
-  - config_name: gmdcsa24
-    data_files:
-      - split: train
-        path: "splits/cs/gmdcsa24/train.csv"
-      - split: validation
-        path: "splits/cs/gmdcsa24/val.csv"
-      - split: test
-        path: "splits/cs/gmdcsa24/test.csv"
-    description: "GMDCSA24 dataset with cross-subject splits"
-  
-  - config_name: le2i
-    data_files:
-      - split: train
-        path: "splits/cs/le2i/train.csv"
-      - split: validation
-        path: "splits/cs/le2i/val.csv"
-      - split: test
-        path: "splits/cs/le2i/test.csv"
-    description: "LE2I dataset with cross-subject splits"
-  
-  - config_name: mcfd
-    data_files:
-      - split: train
-        path: "splits/cs/mcfd/train.csv"
-      - split: validation
-        path: "splits/cs/mcfd/val.csv"
-      - split: test
-        path: "splits/cs/mcfd/test.csv"
-    description: "MCFD dataset with cross-subject splits"
-  
-  - config_name: occu
-    data_files:
-      - split: train
-        path: "splits/cs/occu/train.csv"
-      - split: validation
-        path: "splits/cs/occu/val.csv"
-      - split: test
-        path: "splits/cs/occu/test.csv"
-    description: "OCCU dataset with cross-subject splits"
-  
-  - config_name: up_fall
-    data_files:
-      - split: train
-        path: "splits/cs/up_fall/train.csv"
-      - split: validation
-        path: "splits/cs/up_fall/val.csv"
-      - split: test
-        path: "splits/cs/up_fall/test.csv"
-    description: "UP-Fall dataset with cross-subject splits"
 ---
 [![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>
@@ -270,7 +25,13 @@ configs:
 
 # OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
 
-This repository contains the annotation and split definitions for OmniFall, a comprehensive benchmark that unifies eight public indoor fall datasets and the OOPS-Fall benchmark of genuine accidents captured in the wild under a sixteen-class annotation scheme. The staged datasets use a core subset of ten classes, while OOPS-Fall utilizes the full sixteen-class taxonomy.
+This repository contains annotations, splits, and metadata for OmniFall, a comprehensive benchmark with three components:
+
+- **OF-Staged (OF-Sta):** 8 public staged fall detection datasets (~14h single-view, 112 subjects, 31 camera views)
+- **OF-In-the-Wild (OF-ItW):** Curated genuine accident videos from OOPS (~2.7h, 818 videos)
+- **OF-Synthetic (OF-Syn):** 12,000 synthetic videos generated with Wan 2.2 (~17h) with controlled demographic diversity
+
+All components share a sixteen-class activity taxonomy. Staged datasets use the core ten classes (0-9), while OF-ItW and OF-Syn use the full sixteen classes (0-15).
 
 [OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection](https://arxiv.org/abs/2505.19889)
 
@@ -278,21 +39,9 @@ Also have a look for additional information on our project page:
 
 [OmniFall Project Page](https://simplexsigil.github.io/omnifall/)
 
-## Overview
-
-Falls are the leading cause of fatal injuries among older adults worldwide. While the mechanical event of falling lasts only a fraction of a second, the critical health risk often comes from the ensuing "fallen" state���when a person remains on the ground, potentially injured and unable to call for help.
-
-OmniFall addresses three critical limitations in current fall detection research:
-
-1. **Unified Taxonomy:** Rather than binary fall/no-fall classification, we provide a sixteen-class scheme that distinguishes transient actions (fall, sit down, lie down, stand up) from their static outcomes (fallen, sitting, lying, standing). Staged datasets use the core ten classes (0-9), while OOPS-Fall utilizes the full sixteen classes (0-15) to capture additional activities observed in wild scenarios (kneel_down, kneeling, squat_down, squatting, crawl, jump). The extended classes are infrequent and can be treated as "other" (class 9) for compatibility.
-
-2. **Combined Benchmark:** We unify eight public datasets (14+ hours of video, 112 subjects, 31 camera views) into a single benchmark with standardized train/val/test splits.
-
-3. **In-the-Wild Evaluation:** We include OOPS-Fall, curated from genuine accident videos of the OOPS dataset to test generalization to real-world conditions.
-
 ## Datasets
 
-This benchmark includes annotations for the following datasets:
+### OF-Staged (8 public datasets)
 
 1. **[CMDFall](https://www.mica.edu.vn/perso/Tran-Thi-Thanh-Hai/CMDFALL.html)** (7h 25m single view) - 50 subjects, 7 synchronized views
 2. **[UP Fall](https://sites.google.com/up.edu.mx/har-up/)** (4h 35m) - 17 subjects, 2 synchronized views
@@ -302,16 +51,29 @@ This benchmark includes annotations for the following datasets:
 6. **[EDF](https://doi.org/10.5281/zenodo.15494102)** (13m) - 5 subjects, 2 views synchronized
 7. **[OCCU](https://doi.org/10.5281/zenodo.15494102)** (14m) - 5 subjects, 2 views not synchronized
 8. **[MCFD](https://www.iro.umontreal.ca/~labimage/Dataset/)** (12m) - 1 subject, 8 views
-9. **[OOPS-Fall](https://oops.cs.columbia.edu/data/)** - Curated subset of genuine fall accidents from the OOPS dataset, strong variation in subjects and views.
+
+### OF-ItW (In-the-Wild)
+
+9. **[OOPS-Fall](https://oops.cs.columbia.edu/data/)** (2h 39m) - Curated subset of genuine fall accidents from the OOPS dataset, strong variation in subjects and views.
+
+### OF-Syn (Synthetic)
+
+10. **OF-Syn** (16h 53m) - 12,000 synthetic videos generated with Wan 2.2 video diffusion model. Features controlled demographic diversity across age (6 groups), ethnicity (7 OMB categories), body type (4 BMI bands), gender, and environments. Labels include 19 columns: 7 core annotation fields plus 12 demographic and scene metadata fields.
 
 ## Structure
 
 The repository is organized as follows:
 
-- `labels/` - CSV files containing frame-level annotations for each dataset as well as label2id.csv
-- `splits/` - Train/validation/test splits for cross-subject (CS) and cross-view (CV) evaluation
-  - `splits/cs/` - Cross-subject splits, where training, validation, and test sets contain different subjects
-  - `splits/cv/` - Cross-view splits, where training, validation, and test sets contain different camera views
+- `omnifall.py` - Custom HuggingFace dataset builder (handles all configs)
+- `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)
+- `splits/` - Train/validation/test splits
+  - `splits/cs/` - Cross-subject splits (staged + OOPS)
+  - `splits/cv/` - Cross-view splits (staged + OOPS)
+  - `splits/syn/` - Synthetic splits (random, cross_age, cross_ethnicity, cross_bmi)
+- `videos/metadata.csv` - OF-Syn video-level metadata (12,000 videos)
+- `data_files/syn_frame_wise_labels.tar.zst` - OF-Syn frame-wise HDF5 labels
 
 ### Label Format
 
@@ -347,9 +109,11 @@ Where:
 - `cam`: Camera view ID
 - `dataset`: Name of the dataset
 
-**Note on class usage:** Staged datasets (caucafall, cmdfall, edf, gmdcsa24, le2i, mcfd, occu, up_fall) use only the core ten classes (0-9), as these datasets almost never contain situations where the extended classes (10-15) apply. OOPS-Fall uses the full sixteen-class taxonomy to capture the diverse activities in genuine accident videos. The extended classes are infrequent even in OOPS-Fall and can be treated as "other" (class 9) when working with models trained on staged datasets only.
+**Note on class usage:** Staged datasets (OF-Sta) use only the core ten classes (0-9). OF-ItW and OF-Syn use the full sixteen-class taxonomy. The extended classes (10-15) are infrequent and can be treated as "other" (class 9) for compatibility.
 
-Cam and subject ids in OOPS-Fall are -1, since every video differs from the others and there are no consistent camera views or subjects.
+Cam and subject IDs are -1 for OF-ItW and OF-Syn, since these datasets have no consistent camera views or subjects across videos.
+
+**Note on OF-Syn labels:** The `labels/of-syn.csv` file contains 19 columns: the 7 core fields above plus 12 demographic and scene 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). These additional fields are only available when loading OF-Syn configs.
 
 ### Split Format
 
@@ -362,79 +126,83 @@ path/to/clip
 
 ## Evaluation Protocols
 
-We provide multiple evaluation configurations via the `dataset.yaml` file:
+All configurations are defined in the `omnifall.py` dataset builder and 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)
+
+### OF-Staged Configs
+- `of-sta-cs`: 8 staged datasets, cross-subject splits
+- `of-sta-cv`: 8 staged datasets, cross-view splits
+
+### OF-ItW Config
+- `of-itw`: OOPS-Fall in-the-wild genuine accidents
+
+### OF-Syn Configs
+- `of-syn`: Fixed randomized 80/10/10 split
+- `of-syn-cross-age`: Cross-age split (train: adults, test: children/elderly)
+- `of-syn-cross-ethnicity`: Cross-ethnicity split
+- `of-syn-cross-bmi`: Cross-BMI split (train: normal/underweight, test: obese)
+
+### Cross-Domain Evaluation
+- `of-sta-itw-cs`: Train/val on staged CS, test on OOPS
+- `of-sta-itw-cv`: Train/val on staged CV, test on OOPS
+- `of-syn-itw`: Train/val on OF-Syn random, test on OOPS
 
-### Basic Configurations
-- `default`: Access to all dataset labels (huggingface loads everything into the `train` split by default.)
-- `cs`: Cross-subject splits for all datasets
-- `cv`: Cross-view splits for all datasets
+### Aggregate Configs (staged + OOPS combined)
+- `cs`: Cross-subject splits for all staged + OOPS
+- `cv`: Cross-view splits for all staged + OOPS
 
-### Individual Dataset Configurations
-- `caucafall`, `cmdfall`, `edf`, `gmdcsa24`, `le2i`, `mcfd`, `occu`, `up_fall`, `OOPS`: 
-  Access to individual datasets with their respective cross-subject splits
+### Individual Dataset Configs
+- `caucafall`, `cmdfall`, `edf`, `gmdcsa24`, `le2i`, `mcfd`, `occu`, `up_fall`: Individual datasets with cross-subject splits
 
-### Multi-Dataset Evaluation Protocols
-- `cs-staged`: Cross-subject splits combined across all staged datasets
-- `cv-staged`: Cross-view splits combined across all staged datasets
-- `cs-staged-wild`: Train and validate on staged datasets with cross-subject splits, test on OOPS-Fall
-- `cv-staged-wild`: Train and validate on staged datasets with cross-view splits, test on OOPS-Fall
+### Deprecated Config Names
+
+The following old config names still work but emit a deprecation warning:
+
+| Old Name | Use Instead |
+|---|---|
+| `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` |
 
 ## Examples
 
 ```python
 from datasets import load_dataset
-import pandas as pd
 
-# Load the datasets
-print("Loading datasets...")
+# Load the three main components
+staged = load_dataset("simplexsigil2/omnifall", "of-sta-cs")   # 8 staged datasets
+itw    = load_dataset("simplexsigil2/omnifall", "of-itw")      # OOPS in-the-wild
+syn    = load_dataset("simplexsigil2/omnifall", "of-syn")      # OF-Syn synthetic
 
-# Note: We separate segment labels and split definitions, but hugginface datasets always expects a split.
-# Thats why all labels are in the train split when loaded, but we create the actual splits afterwards.
-labels = load_dataset("simplexsigil2/omnifall", "labels")["train"]
+# Each returns train/validation/test splits with labels already merged
+print(f"Staged train: {len(staged['train'])} segments")
+print(f"ItW test: {len(itw['test'])} segments")
+print(f"Syn train: {len(syn['train'])} segments")
 
-cv_split = load_dataset("simplexsigil2/omnifall", "cv")
-cs_split = load_dataset("simplexsigil2/omnifall", "cs")
-
-# There are many more splits, relevant for the paper:
-# - cv-staged -> Only lab datasets
-# - cs-staged -> Only lab datasets
-# - cv-staged-wild -> Lab datasets for train and val, only OOPS-Fall in test set
-# - cs-staged-wild -> Lab datasets for train and val, only OOPS-Fall in test set
-
-# Convert to pandas DataFrames
-labels_df = pd.DataFrame(labels)
-print(f"Labels dataframe shape: {labels_df.shape}")
-
-# Process each split type (CV and CS)
-for split_name, split_data in [("CV", cv_split), ("CS", cs_split)]:
-    print(f"\n{split_name} Split Processing:")
-
-    # Process each split (train, validation, test)
-    for subset_name, subset in split_data.items():
-        # Convert to DataFrame
-        subset_df = pd.DataFrame(subset)
-
-        # Join with labels on 'path'
-        merged_df = pd.merge(subset_df, labels_df, on="path", how="left")
-
-        # Print statistics
-        print(f"  {subset_name} split: {len(subset_df)} videos, {merged_df.dropna().shape[0]} labelled segments")
-
-        # Print examples
-        if not merged_df.empty:
-            print(f"\n  {subset_name.upper()} EXAMPLES:")
-            random_samples = merged_df.sample(min(3, len(merged_df)))
-            for i, (_, row) in enumerate(random_samples.iterrows()):
-                print(f"  Example {i+1}:")
-                print(f"    Path: {row['path']}")
-                print(f"    Start: {row['start']}")
-                print(f"    End: {row['end']}")
-                print(f"    Label: {row['label']}")
-                print(f"    Subject: {row['subject']}")
-                print(f"    Dataset: {row['dataset']}")
-                print(f"    Camera: {row['cam']}")
-                print()
+# Synthetic data includes demographic metadata (19 columns)
+example = syn['train'][0]
+print(f"Path: {example['path']}, Label: {example['label']}")
+print(f"Age: {example['age_group']}, Ethnicity: {example['race_ethnicity_omb']}")
+
+# Cross-domain: train on staged, test on wild
+cross = load_dataset("simplexsigil2/omnifall", "of-sta-itw-cs")
+print(f"Train on staged: {len(cross['train'])} segments")
+print(f"Test on wild: {len(cross['test'])} segments")
+
+# All labels without splits (for custom splitting)
+labels = load_dataset("simplexsigil2/omnifall", "labels")["train"]
+syn_labels = load_dataset("simplexsigil2/omnifall", "labels-syn")["train"]
 
+# Synthetic frame-wise labels (81 labels per video)
+framewise = load_dataset("simplexsigil2/omnifall", "of-syn", framewise=True)
+print(f"Frame labels: {framewise['train'][0]['frame_labels'][:5]}...")  # [1, 1, 1, ...]
 ```
 
 ## Label definitions
@@ -456,9 +224,9 @@ The benchmark uses a sixteen-class taxonomy. Staged datasets use classes 0-9, wh
 - **`8|standing`** - Standing around without walking.
 - **`9|other`** - Any other activity, including e.g. walking while pushing an object like a chair.
 
-### Extended Classes (10-15, OOPS-Fall only)
+### Extended Classes (10-15, OF-ItW and OF-Syn)
 
-These classes capture additional activities observed in genuine accident videos. They are infrequent even in OOPS-Fall and do not occur in staged datasets.
+These classes capture additional activities observed in genuine accident and synthetic videos. They are infrequent and do not occur in staged datasets.
 
 - **`10|kneel_down`** - Transitioning from standing or another posture to a kneeling position.
 - **`11|kneeling`** - Being in a kneeling position (static posture).
@@ -479,13 +247,13 @@ Let's say we see a person `walk`, then `fall`. The first frame which indicates a
 
 ## Label Sequences
 
-There are some natural sequences of labels like `fall`, `fallen` and `stand_up`. However, it is not always the case that these appear together. Sometimes the person might directly stand up again without any time at rest on the ground, in this case there is no `fallen` segment. Likewise sometimes there is no sitting segment.
+There are some natural sequences of labels like `fall`, `fallen` and `stand_up`. However, it is not always the case that these appear together. Sometimes the person might directly stand up again without any time at rest on the ground, in this case there is no `fallen` segment. Likewise sometimes there is no sitting segment after sitting down (e.g. if the person immediately stands up again).
 
 Lying down can be on a bed or on the ground, it is intentional in contrast to `fall`. There are falls which follow `sit_down` or `lie_down` if it is from a chair or from a bed.
 
 When a person is lying in a bed and getting up to sit in the bed we label this as `stand_up`, even if the person is still sitting in the bed. A sequence could then be `lying`, `stand_up`, `sitting`, `stand_up` to describe a person which first lies down, then gets into a sitting position, waits a little, then gets fully up.
 
-Sometimes it is not perfectly clear if it is `sit_down` followed by `lie_down` or simply `lie_down`. This depends on there being a moment of rest or not. If the person spends a short amount of time in the sitting position it is the former, if the person directly goes from `sit_down` to `lie_down` without rest it is only labeled `lie_down`. Similar thoughts apply to `stand_up`.
+Sometimes it is not perfectly clear if it is `sit_down` followed by `lie_down` or simply `lie_down`. This depends on there being a moment of rest or not. If the person spends a short amount of time in the sitting position it is the former, if the person directly goes from `standing` over `sit_down` to `lie_down` without rest it is only labeled `lie_down`. Similar thoughts apply to `stand_up`.
 
 In the video at the bottom of this page we show how we leveraged VGG VIA to perform the annotations. Note, that we pre-load the original dataset labels as visual aid, but relabel the whole video with our label definitions. In the video below, CMDFall is shown, which already provides relatively detailed labels, the original labels of other datasets are more sparse. Additionally, even CMDFall does not label all frames, but only specific segments, while our labels cover mostly every frame.
 
@@ -497,7 +265,7 @@ If you use OmniFall in your research, please cite our paper (will be updated soo
 
 ```bibtex
 @misc{omnifall,
-      title={OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection}, 
+      title={OmniFall: From Staged Through Synthetic to Wild, A Unified Multi-Domain Dataset for Robust Fall Detection}, 
       author={David Schneider and Zdravko Marinov and Rafael Baur and Zeyun Zhong and Rodi Düger and Rainer Stiefelhagen},
       year={2025},
       eprint={2505.19889},

data_files/syn_frame_wise_labels.tar.zst

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f6025389eade4c047b56a92c37b3f34b43e380fe9214a421f9b50660ecc3113
+size 356098

labels/label2id.csv

@@ -0,0 +1,17 @@
+label,id
+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

omnifall.py

@@ -0,0 +1,928 @@
+"""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 warnings
+import pandas as pd
+import datasets
+from datasets import (
+    BuilderConfig,
+    GeneratorBasedBuilder,
+    Features,
+    Value,
+    ClassLabel,
+    Sequence,
+    SplitGenerator,
+    Split,
+)
+
+_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"
+
+
+# ---- 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).
+        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,
+        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.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,
+            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()
+
+        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)
+
+        # Metadata config
+        if cfg.config_type == "metadata":
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "metadata", "metadata_path": "videos/metadata.csv"},
+                ),
+            ]
+
+        # 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"
+            )
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": "videos/metadata.csv",
+                        "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):
+        """Generate split generators for labels-type configs."""
+        if cfg.data_source == "syn":
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={"mode": "csv_direct", "filepath": _SYN_LABEL_FILE},
+                ),
+            ]
+        elif cfg.data_source == "staged+itw":
+            return [
+                SplitGenerator(
+                    name=Split.TRAIN,
+                    gen_kwargs={
+                        "mode": "csv_multi",
+                        "filepaths": _STAGED_LABEL_FILES + [_ITW_LABEL_FILE],
+                    },
+                ),
+            ]
+        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)
+        elif cfg.data_source == "itw":
+            return self._itw_splits(cfg)
+        elif cfg.data_source == "staged+itw":
+            return self._aggregate_splits(cfg)
+        elif cfg.data_source in _STAGED_DATASETS:
+            return self._individual_splits(cfg)
+        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 _make_split_merge_generators(self, split_type, split_files_fn, label_files):
+        """Helper to create train/val/test SplitGenerators for split_merge mode."""
+        return [
+            SplitGenerator(
+                name=split_enum,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": split_files_fn(csv_name),
+                    "label_files": label_files,
+                },
+            )
+            for split_enum, csv_name in [
+                (Split.TRAIN, "train"),
+                (Split.VALIDATION, "val"),
+                (Split.TEST, "test"),
+            ]
+        ]
+
+    def _staged_splits(self, cfg):
+        """OF-Staged: 8 datasets combined with CS or CV splits."""
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            st,
+            lambda sn: self._staged_split_files(st, sn),
+            _STAGED_LABEL_FILES,
+        )
+
+    def _itw_splits(self, cfg):
+        """OF-ItW: OOPS-Fall (CS=CV identical)."""
+        st = cfg.split_type
+        return self._make_split_merge_generators(
+            st,
+            lambda sn: [f"splits/{st}/OOPS/{sn}.csv"],
+            [_ITW_LABEL_FILE],
+        )
+
+    def _aggregate_splits(self, cfg):
+        """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(
+            st,
+            lambda sn: self._staged_split_files(st, sn) + [f"splits/{st}/OOPS/{sn}.csv"],
+            all_labels,
+        )
+
+    def _individual_splits(self, cfg):
+        """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(
+            st,
+            lambda sn: [f"splits/{st}/{ds_name}/{sn}.csv"],
+            [label_file],
+        )
+
+    def _syn_splits(self, cfg, dl_manager):
+        """OF-Syn split strategies."""
+        st = cfg.split_type
+        split_dir = f"splits/syn/{st}"
+
+        if cfg.framewise:
+            archive_path = dl_manager.download_and_extract(
+                "data_files/syn_frame_wise_labels.tar.zst"
+            )
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "framewise",
+                        "hdf5_dir": archive_path,
+                        "metadata_path": "videos/metadata.csv",
+                        "split_file": f"{split_dir}/{csv_name}.csv",
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+
+        if cfg.paths_only:
+            return [
+                SplitGenerator(
+                    name=split_enum,
+                    gen_kwargs={
+                        "mode": "paths_only",
+                        "split_file": f"{split_dir}/{csv_name}.csv",
+                    },
+                )
+                for split_enum, csv_name in [
+                    (Split.TRAIN, "train"),
+                    (Split.VALIDATION, "val"),
+                    (Split.TEST, "test"),
+                ]
+            ]
+
+        return self._make_split_merge_generators(
+            st,
+            lambda sn: [f"{split_dir}/{sn}.csv"],
+            [_SYN_LABEL_FILE],
+        )
+
+    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"
+
+        # Determine train/val files
+        if cfg.train_source == "staged":
+            train_files_fn = lambda sn: self._staged_split_files(train_st, sn)
+            train_labels = _STAGED_LABEL_FILES
+        elif cfg.train_source == "syn":
+            train_files_fn = lambda sn: [f"splits/syn/{train_st}/{sn}.csv"]
+            train_labels = [_SYN_LABEL_FILE]
+        else:
+            raise ValueError(f"Unsupported train_source: {cfg.train_source}")
+
+        # Determine test files
+        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}")
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": train_files_fn("train"),
+                    "label_files": train_labels,
+                },
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": train_files_fn("val"),
+                    "label_files": train_labels,
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "mode": "split_merge",
+                    "split_files": test_split_files,
+                    "label_files": test_labels,
+                },
+            ),
+        ]
+
+    # ---- 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):
+        """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():
+            yield idx, self._row_to_example(row)
+
+    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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