Datasets:

Zovda
/

dm0010-egocentric-physical-interaction-samples

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
asset_id: string
frame_index: int64
timestamp_ms: int64
annotation_source: string
is_ground_truth: bool
arms: list<item: struct<side: string, hand_bbox_xyxy: list<item: double>, visible_forearm_axis: struct<wri (... 153 chars omitted)
  child 0, item: struct<side: string, hand_bbox_xyxy: list<item: double>, visible_forearm_axis: struct<wrist_xy: list (... 141 chars omitted)
      child 0, side: string
      child 1, hand_bbox_xyxy: list<item: double>
          child 0, item: double
      child 2, visible_forearm_axis: struct<wrist_xy: list<item: int64>, axis_end_xy: list<item: double>, pca_anisotropy: double, confide (... 12 chars omitted)
          child 0, wrist_xy: list<item: int64>
              child 0, item: int64
          child 1, axis_end_xy: list<item: double>
              child 0, item: double
          child 2, pca_anisotropy: double
          child 3, confidence: double
      child 3, elbow: null
      child 4, shoulder: null
      child 5, limitation: string
end_ms: int64
start_ms: int64
label: string
to
{'start_ms': Value('int64'), 'end_ms': Value('int64'), 'label': Value('string'), 'annotation_source': Value('string'), 'is_ground_truth': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              asset_id: string
              frame_index: int64
              timestamp_ms: int64
              annotation_source: string
              is_ground_truth: bool
              arms: list<item: struct<side: string, hand_bbox_xyxy: list<item: double>, visible_forearm_axis: struct<wri (... 153 chars omitted)
                child 0, item: struct<side: string, hand_bbox_xyxy: list<item: double>, visible_forearm_axis: struct<wrist_xy: list (... 141 chars omitted)
                    child 0, side: string
                    child 1, hand_bbox_xyxy: list<item: double>
                        child 0, item: double
                    child 2, visible_forearm_axis: struct<wrist_xy: list<item: int64>, axis_end_xy: list<item: double>, pca_anisotropy: double, confide (... 12 chars omitted)
                        child 0, wrist_xy: list<item: int64>
                            child 0, item: int64
                        child 1, axis_end_xy: list<item: double>
                            child 0, item: double
                        child 2, pca_anisotropy: double
                        child 3, confidence: double
                    child 3, elbow: null
                    child 4, shoulder: null
                    child 5, limitation: string
              end_ms: int64
              start_ms: int64
              label: string
              to
              {'start_ms': Value('int64'), 'end_ms': Value('int64'), 'label': Value('string'), 'annotation_source': Value('string'), 'is_ground_truth': Value('bool')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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start_ms int64	end_ms int64	label string	annotation_source string	is_ground_truth bool
200	12,200	manipulation_candidate	heuristic_model_estimate	false
12,200	13,300	idle_or_low_motion	heuristic_model_estimate	false
13,300	15,400	manipulation_candidate	heuristic_model_estimate	false
15,400	18,000	idle_or_low_motion	heuristic_model_estimate	false
18,600	20,900	idle_or_low_motion	heuristic_model_estimate	false
20,900	21,400	manipulation_candidate	heuristic_model_estimate	false
21,400	22,600	idle_or_low_motion	heuristic_model_estimate	false
22,600	23,100	manipulation_candidate	heuristic_model_estimate	false
23,100	25,900	idle_or_low_motion	heuristic_model_estimate	false
25,900	26,400	manipulation_candidate	heuristic_model_estimate	false
26,400	29,600	idle_or_low_motion	heuristic_model_estimate	false
29,900	35,700	idle_or_low_motion	heuristic_model_estimate	false
35,800	38,800	idle_or_low_motion	heuristic_model_estimate	false
39,200	39,700	idle_or_low_motion	heuristic_model_estimate	false
39,700	40,200	manipulation_candidate	heuristic_model_estimate	false
40,200	41,100	idle_or_low_motion	heuristic_model_estimate	false
41,100	42,300	manipulation_candidate	heuristic_model_estimate	false
42,300	42,900	idle_or_low_motion	heuristic_model_estimate	false
42,900	45,400	manipulation_candidate	heuristic_model_estimate	false
45,400	69,200	idle_or_low_motion	heuristic_model_estimate	false
69,200	70,700	manipulation_candidate	heuristic_model_estimate	false
70,700	71,300	idle_or_low_motion	heuristic_model_estimate	false
71,300	73,400	manipulation_candidate	heuristic_model_estimate	false
73,400	75,700	idle_or_low_motion	heuristic_model_estimate	false
75,700	76,400	manipulation_candidate	heuristic_model_estimate	false
76,400	76,900	idle_or_low_motion	heuristic_model_estimate	false
77,300	81,600	idle_or_low_motion	heuristic_model_estimate	false
81,600	82,300	manipulation_candidate	heuristic_model_estimate	false
82,300	85,600	idle_or_low_motion	heuristic_model_estimate	false
85,600	88,800	manipulation_candidate	heuristic_model_estimate	false
88,800	89,300	idle_or_low_motion	heuristic_model_estimate	false
89,300	89,900	manipulation_candidate	heuristic_model_estimate	false
89,900	94,300	idle_or_low_motion	heuristic_model_estimate	false
94,700	95,400	idle_or_low_motion	heuristic_model_estimate	false
95,400	97,700	manipulation_candidate	heuristic_model_estimate	false
98,100	98,900	manipulation_candidate	heuristic_model_estimate	false
98,900	102,200	idle_or_low_motion	heuristic_model_estimate	false
102,200	105,400	manipulation_candidate	heuristic_model_estimate	false
105,800	106,400	manipulation_candidate	heuristic_model_estimate	false
106,400	106,900	idle_or_low_motion	heuristic_model_estimate	false
107,300	108,900	idle_or_low_motion	heuristic_model_estimate	false
108,900	109,400	manipulation_candidate	heuristic_model_estimate	false
109,400	110,500	idle_or_low_motion	heuristic_model_estimate	false
110,500	111,000	manipulation_candidate	heuristic_model_estimate	false
111,000	114,600	idle_or_low_motion	heuristic_model_estimate	false
114,600	115,200	manipulation_candidate	heuristic_model_estimate	false
115,200	115,700	idle_or_low_motion	heuristic_model_estimate	false
115,700	116,300	manipulation_candidate	heuristic_model_estimate	false
116,300	117,000	idle_or_low_motion	heuristic_model_estimate	false
117,000	117,700	manipulation_candidate	heuristic_model_estimate	false
117,700	118,800	idle_or_low_motion	heuristic_model_estimate	false
119,200	119,800	idle_or_low_motion	heuristic_model_estimate	false
119,800	120,600	manipulation_candidate	heuristic_model_estimate	false
120,600	121,300	idle_or_low_motion	heuristic_model_estimate	false
121,300	126,200	manipulation_candidate	heuristic_model_estimate	false
126,400	127,500	manipulation_candidate	heuristic_model_estimate	false
127,500	128,500	idle_or_low_motion	heuristic_model_estimate	false
128,500	129,600	manipulation_candidate	heuristic_model_estimate	false
130,000	132,600	manipulation_candidate	heuristic_model_estimate	false
132,900	135,000	manipulation_candidate	heuristic_model_estimate	false
135,000	135,700	idle_or_low_motion	heuristic_model_estimate	false
135,700	139,900	manipulation_candidate	heuristic_model_estimate	false
139,900	140,800	idle_or_low_motion	heuristic_model_estimate	false
140,800	141,900	manipulation_candidate	heuristic_model_estimate	false
142,200	144,100	manipulation_candidate	heuristic_model_estimate	false
144,500	147,400	manipulation_candidate	heuristic_model_estimate	false
147,400	148,200	idle_or_low_motion	heuristic_model_estimate	false
148,200	149,700	manipulation_candidate	heuristic_model_estimate	false
149,700	151,500	idle_or_low_motion	heuristic_model_estimate	false
151,500	160,000	manipulation_candidate	heuristic_model_estimate	false
160,000	161,400	idle_or_low_motion	heuristic_model_estimate	false
161,400	161,900	manipulation_candidate	heuristic_model_estimate	false
161,900	163,300	idle_or_low_motion	heuristic_model_estimate	false
163,300	173,900	manipulation_candidate	heuristic_model_estimate	false
173,900	174,900	idle_or_low_motion	heuristic_model_estimate	false
174,900	176,300	manipulation_candidate	heuristic_model_estimate	false
176,300	177,000	idle_or_low_motion	heuristic_model_estimate	false
177,000	180,100	manipulation_candidate	heuristic_model_estimate	false
180,100	181,100	idle_or_low_motion	heuristic_model_estimate	false
181,100	182,400	manipulation_candidate	heuristic_model_estimate	false
182,400	183,100	idle_or_low_motion	heuristic_model_estimate	false
183,100	184,900	manipulation_candidate	heuristic_model_estimate	false
184,900	185,700	idle_or_low_motion	heuristic_model_estimate	false
185,700	191,900	manipulation_candidate	heuristic_model_estimate	false
191,900	192,500	idle_or_low_motion	heuristic_model_estimate	false
192,500	194,400	manipulation_candidate	heuristic_model_estimate	false
194,400	195,000	idle_or_low_motion	heuristic_model_estimate	false
195,000	197,200	manipulation_candidate	heuristic_model_estimate	false
197,200	198,000	idle_or_low_motion	heuristic_model_estimate	false
198,000	198,600	manipulation_candidate	heuristic_model_estimate	false
199,000	201,000	manipulation_candidate	heuristic_model_estimate	false
201,000	201,800	idle_or_low_motion	heuristic_model_estimate	false
201,800	202,300	manipulation_candidate	heuristic_model_estimate	false
202,300	205,600	idle_or_low_motion	heuristic_model_estimate	false
205,600	210,800	manipulation_candidate	heuristic_model_estimate	false
210,800	211,600	idle_or_low_motion	heuristic_model_estimate	false
211,600	213,200	manipulation_candidate	heuristic_model_estimate	false
213,200	214,100	idle_or_low_motion	heuristic_model_estimate	false
214,100	216,000	manipulation_candidate	heuristic_model_estimate	false
216,200	216,900	manipulation_candidate	heuristic_model_estimate	false

End of preview.

DM0010 · Egocentric Physical Interaction Samples / 第一人称物理世界交互数据样例

Item / 项目	Value / 值
Dataset ID / 数据集编号	DM0010
Files / 数据文件	12
Size / 数据体积	185.97 MB
Provided by / 提供方	Zovda
Contact / 联系邮箱	lucas@zovda.com

Overview / 简介

An end-to-end delivery sample of one egocentric capture session: master and proxy media, an annotated preview, and seven structured annotation types covering actions, arm pose, camera motion, hands, objects, hand-object interaction and key steps.

第一人称采集 Session 的端到端交付样例：主视频、代理视频、叠加标注预览与七类结构化标注，覆盖动作、手臂姿态、相机运动、手部、物体、手物交互与关键步骤。

What's inside / 数据内容

media/proxies/: 10 fps proxy video for fast review, annotation and QA.
media/proxies/：10fps 代理视频，供快速浏览、标注与质检。
media/annotated_previews/: review video with annotation overlays.
media/annotated_previews/：叠加标注的审核预览视频。
media/masters_sanitized/: compliance-processed master video (large; contact us to obtain).
media/masters_sanitized/：完成合规处理的主版视频（体积较大，如需获取请联系我们）。
annotations/: seven JSONL annotation types — actions, arm_pose, camera_motion, hands, interactions, keysteps, objects.
annotations/：七类逐帧/逐段 JSONL 标注——actions、arm_pose、camera_motion、hands、interactions、keysteps、objects。
docs/: data card, annotation schema (ANNOTATION_SCHEMA_RGB_ONLY_L1) and delivery audit report.
docs/：数据卡（DATACARD）、标注 Schema（ANNOTATION_SCHEMA_RGB_ONLY_L1）与交付审计报告。

Specifications / 规格

Organisation: by capture session; this sample is P001_S001_T001
组织方式：按 Session（采集场次）组织，本样例为 P001_S001_T001
Annotation format: JSONL, per frame or per segment, timestamped
标注格式：JSONL，逐帧或逐段，带时间戳
Media: master MOV, 10 fps proxy MP4, annotated preview MP4
媒体：主版 MOV、10fps 代理 MP4、标注预览 MP4

Suitable tasks / 适用任务

Embodied-AI / robot-manipulation pretraining
具身智能/机器人操作数据预训练
Hand and object pose estimation, hand-object interaction detection
手部与物体位姿估计、手物交互检测
Egocentric action recognition and key-step segmentation
第一人称动作识别与任务步骤分割
Camera-motion modelling and scene understanding
相机运动建模与场景理解

Files / 文件清单

#	File	Size
1	`ego-delivery/annotations/actions/P001_S001_T001_actions.jsonl`	18.53 KB
2	`ego-delivery/annotations/arm_pose/P001_S001_T001_visible_arm_estimates.jsonl`	239.06 KB
3	`ego-delivery/annotations/camera_motion/P001_S001_T001_camera_motion_2d.jsonl`	177.93 KB
4	`ego-delivery/annotations/hands/P001_S001_T001_hands.jsonl`	7.94 MB
5	`ego-delivery/annotations/interactions/P001_S001_T001_hand_object_candidates.jsonl`	124.18 KB
6	`ego-delivery/annotations/keysteps/P001_S001_T001_keysteps.jsonl`	228 B
7	`ego-delivery/annotations/objects/P001_S001_T001_objects_l1.jsonl`	1.45 MB
8	`ego-delivery/docs/ANNOTATION_SCHEMA_RGB_ONLY_L1.md`	3.39 KB
9	`ego-delivery/docs/DATACARD.md`	1.09 KB
10	`ego-delivery/docs/DELIVERABLES_AND_SOW_AUDIT.md`	30.59 KB
11	`ego-delivery/media/annotated_previews/S001_annotated_h264.mp4`	71.42 MB
12	`ego-delivery/media/proxies/P001_S001_T001_proxy_10fps.mp4`	104.59 MB

Usage / 使用方式

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Zovda/dm0010-egocentric-physical-interaction-samples",
    repo_type="dataset",
    local_dir="./dm0010-egocentric-physical-interaction-samples",
)

Rights and disclaimer / 授权与声明

Collected, cleaned and annotated by Zovda. Samples are shared to evaluate data structure and annotation conventions; contact us for commercial licensing at scale.

本数据集由 Zovda 采集、清洗与标注，样例可用于评估数据结构与标注口径。规模化商用授权请联系我们。

This dataset is shared to demonstrate data organisation, technical specifications and delivery structure. For formal licensing, large-scale access or custom production, contact lucas@zovda.com.

本数据集仅用于展示数据组织方式、技术规格与交付结构。正式授权、规模化取数与定制生产请联系 lucas@zovda.com。

About Zovda / 关于 Zovda

Zovda builds, cleans, annotates and packages training data across video, audio, images, e-commerce image-text pairs, exam question banks, embodied AI and coding-agent corpora.

Zovda 提供多模态与文本训练语料的数据采集、清洗、标注与结构化交付服务，覆盖视频、音频、图像、电商图文、K12 与职业题库、具身智能、代码智能体等方向。

Contact / 联系方式: lucas@zovda.com

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