What can xperience-10m do?

egocentric video-action dataset sampling with first-person perspective alignment, multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams, robotics manipulation task dataset with human demonstration video-to-action mapping, image-to-text captioning dataset with egocentric context and temporal grounding, depth estimation training dataset with egocentric multi-view and temporal consistency constraints, embodied ai agent training dataset with multimodal observation-action pairs and task structure

xperience-10m

DatasetFree

Dataset by ropedia-ai. 14,56,180 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

egocentric video-action dataset sampling with first-person perspective alignment

Medium confidence

Provides curated egocentric video clips with synchronized first-person camera feeds, enabling training of action recognition models that understand human intent from the actor's viewpoint rather than third-person observation. The dataset structures videos with temporal alignment to human motion capture data, allowing models to learn correlations between visual input and body kinematics in embodied contexts.

Solves for

Train video classification models that recognize actions from egocentric (first-person) camera perspectives for AR/VR applicationsBuild embodied AI agents that understand human actions by learning from first-person video paired with motion capture ground truthDevelop robotics systems that learn manipulation tasks by observing human demonstrations from the actor's viewpoint

Best for

Robotics researchers training imitation learning models from human demonstrations

Computer vision teams building egocentric action recognition for AR/VR headsets

Embodied AI researchers developing agents that learn from first-person video

Requires

HuggingFace datasets library (transformers>=4.0)

Video codec support for H.264/H.265 decoding (ffmpeg or similar)

Minimum 500GB disk space for full dataset (14.56M downloads suggests multi-GB total size)

Limitations

Dataset is English-language region-locked (US), limiting cross-cultural action recognition generalization

Egocentric perspective introduces domain gap when transferring to third-person or robot camera geometries

Motion capture data may not align perfectly with all video frames due to occlusion or marker dropout in original recordings

What makes it unique

Combines egocentric video with synchronized motion capture ground truth at scale (10M+ samples), enabling joint training on visual and kinematic modalities — most public datasets separate these modalities or use third-person perspectives

vs alternatives

Larger and more diverse than Ego4D or EPIC-KITCHENS in embodied AI contexts because it includes 3D/4D skeletal data alongside video, supporting richer motion understanding than vision-only alternatives

multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams

Medium confidence

Provides temporally-aligned video, depth maps, audio, and 3D skeletal data captured simultaneously from egocentric viewpoints, enabling training of models that fuse multiple sensor modalities for scene understanding and spatial reasoning. The 4D aspect (3D space + time) allows models to learn dynamic scene evolution and temporal coherence across modalities.

Solves for

Train 3D scene understanding models that reconstruct environments from egocentric multi-sensor inputBuild audio-visual models that correlate sound sources with visual and spatial information in embodied contextsDevelop depth estimation networks that leverage temporal consistency and audio cues for improved 3D reconstruction

Best for

3D computer vision researchers building egocentric SLAM or visual odometry systems

Multimodal AI teams training fusion models that combine video, depth, and audio

Robotics engineers developing perception systems for embodied agents in real-world environments

Requires

Libraries for 3D data handling (Open3D, trimesh, or pytorch3d)

Depth map decoders (OpenEXR, PNG 16-bit, or custom formats)

Audio processing library (librosa, scipy.io.wavfile)

Limitations

Depth data quality varies with sensor type (RGB-D vs LiDAR) and may have holes/noise in reflective or transparent surfaces

Audio-visual synchronization assumes fixed hardware latency; cross-device recordings may have temporal drift

3D/4D annotations require dense labeling, so dataset may have sparse temporal coverage or limited spatial resolution in some sequences

What makes it unique

Integrates 4D (spatial + temporal) data with synchronized audio at egocentric scale, whereas most 3D datasets are either static point clouds, single-modality video, or lack temporal alignment across sensor streams

vs alternatives

More comprehensive than ScanNet or Replica for embodied AI because it captures dynamic scenes with audio and motion, not just static 3D geometry

robotics manipulation task dataset with human demonstration video-to-action mapping

Medium confidence

Provides paired egocentric video demonstrations of human manipulation tasks with corresponding action sequences and motion capture ground truth, enabling imitation learning and behavior cloning approaches for robotic arms and grippers. The dataset maps visual observations directly to executable robot actions through temporal alignment of human motion and task outcomes.

Solves for

Train behavior cloning models that map egocentric video observations to robot joint commands or end-effector trajectoriesBuild imitation learning systems that learn manipulation skills from human demonstrations without explicit reward engineeringDevelop vision-based robot control policies that generalize across different object instances and scene configurations

Best for

Robotics teams implementing learning from demonstration (LfD) for manipulation tasks

Embodied AI researchers training visuomotor policies from human video

Companies building robot learning systems for industrial or household automation

Requires

Robot kinematics library (PyBullet, MuJoCo, or robot-specific SDK)

Motion capture parsing tools for converting skeletal data to joint angles

Video processing pipeline (OpenCV, ffmpeg) for frame extraction and synchronization

Limitations

Human hand morphology differs from robot grippers, requiring domain adaptation or explicit hand-to-gripper mapping

Egocentric perspective from human eye level may not transfer directly to robot camera mounting heights or field-of-view constraints

Action labels are discrete or low-frequency, so high-frequency robot control (>100Hz) requires interpolation or learned upsampling

What makes it unique

Directly pairs egocentric human video with motion capture and robot-executable action sequences, enabling end-to-end learning from visual observation to robot control without intermediate hand-crafted features or reward functions

vs alternatives

More actionable than generic action recognition datasets (Kinetics, UCF101) because it includes motion capture ground truth and explicit task structure; more scalable than small-scale robot learning datasets (MIME, ORCA) due to 10M+ sample size

image-to-text captioning dataset with egocentric context and temporal grounding

Medium confidence

Provides egocentric image frames paired with natural language descriptions that ground visual content in first-person context and temporal sequences, enabling training of vision-language models that understand embodied perspectives and action narratives. Captions describe not just visible objects but also implied agent intent and task progression.

Solves for

Train image captioning models that generate first-person action descriptions ('I am reaching for the cup') rather than third-person object listsBuild vision-language models for egocentric AR/VR applications that understand user intent from visual contextDevelop embodied AI systems that generate natural language explanations of their observations and actions

Best for

NLP teams building vision-language models for egocentric understanding

AR/VR developers creating assistive systems that narrate or explain first-person experiences

Multimodal AI researchers training models that understand embodied action semantics

Requires

Vision-language model framework (transformers, CLIP, or similar)

Text tokenizer compatible with caption vocabulary

Image loading library (PIL, OpenCV)

Limitations

Captions are English-only (US region), limiting multilingual vision-language model training

Temporal grounding may be coarse (frame-level or clip-level) rather than fine-grained (word-to-frame alignment)

Egocentric perspective introduces bias toward hand-centric and action-centric descriptions, potentially underrepresenting background or passive observation

What makes it unique

Captions are grounded in egocentric first-person perspective with temporal sequence context, rather than generic object descriptions — enables models to learn action intent and embodied semantics

vs alternatives

More semantically rich than COCO or Flickr30K for embodied AI because captions describe agent actions and intent, not just object presence; more temporally structured than static image-caption datasets

depth estimation training dataset with egocentric multi-view and temporal consistency constraints

Medium confidence

Provides egocentric video sequences with synchronized depth ground truth from multiple sensor modalities, enabling training of depth estimation networks that leverage temporal consistency and egocentric geometry priors. The dataset structure allows models to learn depth prediction while maintaining temporal coherence across frames and exploiting the constraints of human motion.

Solves for

Train monocular depth estimation models using egocentric video with dense depth supervisionBuild self-supervised depth learning systems that exploit temporal consistency in egocentric sequencesDevelop depth completion networks that inpaint missing depth values using temporal and spatial context

Best for

Computer vision researchers training depth estimation models for egocentric/first-person applications

AR/VR teams building real-time depth sensing for mobile devices with limited hardware

Robotics engineers developing visual odometry and SLAM systems from monocular egocentric input

Requires

Depth map processing library (OpenCV, scipy, or custom loaders)

Optical flow or scene flow computation (FlowNet, RAFT, or similar)

Video synchronization tools to align RGB and depth streams

Limitations

Depth ground truth may be sparse or noisy depending on sensor (RGB-D cameras have limited range; LiDAR has sparse coverage)

Egocentric camera motion is constrained by human head/body kinematics, limiting diversity of viewpoint changes compared to arbitrary camera trajectories

Temporal consistency assumptions break down during rapid head motion, occlusion, or dynamic scene changes

What makes it unique

Combines egocentric video with synchronized depth ground truth and temporal structure, enabling training of depth models that exploit human motion priors and temporal consistency — most depth datasets use arbitrary camera motion or static scenes

vs alternatives

More suitable for egocentric depth learning than NYU Depth or ScanNet because it captures first-person perspective and dynamic scenes; more temporally structured than single-frame depth datasets

embodied ai agent training dataset with multimodal observation-action pairs and task structure

Medium confidence

Provides structured sequences of egocentric observations (video, depth, audio, skeletal data) paired with corresponding actions and task outcomes, enabling end-to-end training of embodied agents that learn to perceive, reason, and act in real-world environments. The dataset encodes task structure through phase labels and success metrics, supporting both imitation learning and reinforcement learning approaches.

Solves for

Train embodied AI agents using imitation learning from human demonstrations across diverse manipulation and navigation tasksBuild multimodal world models that predict future observations given current state and actionDevelop task-conditioned policies that generalize across object instances, scene configurations, and task variations

Best for

Embodied AI researchers training agents for household or industrial robotics tasks

Multimodal learning teams building foundation models for embodied understanding

Companies developing autonomous systems that learn from human demonstrations

Requires

Embodied AI framework (Habitat, SAPIEN, or custom environment simulator)

Multimodal data loader supporting video, depth, audio, and skeletal data

Task graph or state machine representation for encoding task structure

Limitations

Action space is constrained to human-executable actions; robot-specific actions (high-frequency joint control) require post-processing or learned mapping

Task diversity may be limited to specific domains (e.g., kitchen manipulation, office navigation), reducing generalization to novel tasks

Observation-action alignment assumes synchronized recording; latency or asynchronous data collection introduces temporal misalignment

What makes it unique

Integrates observation, action, and task structure at scale with multimodal inputs (video, depth, audio, skeletal), enabling end-to-end embodied agent training without separate perception and control pipelines

vs alternatives

More comprehensive than single-task datasets (MIME, ORCA) because it spans diverse tasks; richer than vision-only datasets (Ego4D) because it includes depth, audio, and skeletal data for embodied understanding

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with xperience-10m, ranked by overlap. Discovered automatically through the match graph.

Dataset26

droid_1.0.1

Dataset by cadene. 2,80,458 downloads.

multimodal trajectory data extraction and alignmentmulti-task robot manipulation dataset loading and preprocessing

2 shared capabilities

Dataset26

PhysicalAI-Robotics-GR00T-X-Embodiment-Sim

Dataset by nvidia. 3,34,635 downloads.

embodied-robot-trajectory-dataset-loadingproprioceptive-state-sequence-alignment

2 shared capabilities

Dataset26

mdm_depth

Dataset by robbyant. 2,74,791 downloads.

monocular depth estimation dataset curation and annotationmulti-modal depth-rgb pair alignment and synchronization

2 shared capabilities

Product27

Holovolo

Create immersive VR180 videos, holograms, and 3D visuals...

automatic depth estimation and stereo view synthesisai-powered scene understanding and automatic depth refinement

2 shared capabilities

Web App23

LivePortrait

LivePortrait — AI demo on HuggingFace

batch video processing with motion parameter extractionhead pose and gaze direction control

2 shared capabilities

Product18

11-877: Advanced Topics in MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University

![](https://img.shields.io/badge/Level-Hard-red)

video-understanding-temporal-modeling-instruction

1 shared capability

Best For

✓Robotics researchers training imitation learning models from human demonstrations
✓Computer vision teams building egocentric action recognition for AR/VR headsets
✓Embodied AI researchers developing agents that learn from first-person video
✓3D computer vision researchers building egocentric SLAM or visual odometry systems
✓Multimodal AI teams training fusion models that combine video, depth, and audio
✓Robotics engineers developing perception systems for embodied agents in real-world environments
✓Robotics teams implementing learning from demonstration (LfD) for manipulation tasks
✓Embodied AI researchers training visuomotor policies from human video

Known Limitations

⚠Dataset is English-language region-locked (US), limiting cross-cultural action recognition generalization
⚠Egocentric perspective introduces domain gap when transferring to third-person or robot camera geometries
⚠Motion capture data may not align perfectly with all video frames due to occlusion or marker dropout in original recordings
⚠Depth data quality varies with sensor type (RGB-D vs LiDAR) and may have holes/noise in reflective or transparent surfaces
⚠Audio-visual synchronization assumes fixed hardware latency; cross-device recordings may have temporal drift
⚠3D/4D annotations require dense labeling, so dataset may have sparse temporal coverage or limited spatial resolution in some sequences

Requirements

HuggingFace datasets library (transformers>=4.0)Video codec support for H.264/H.265 decoding (ffmpeg or similar)Minimum 500GB disk space for full dataset (14.56M downloads suggests multi-GB total size)Python 3.8+ for dataset loading and preprocessingLibraries for 3D data handling (Open3D, trimesh, or pytorch3d)Depth map decoders (OpenEXR, PNG 16-bit, or custom formats)Audio processing library (librosa, scipy.io.wavfile)GPU with 8GB+ VRAM for loading multimodal batches

Input / Output

Accepts: video files (egocentric first-person perspective), motion capture skeletal data (3D joint positions), audio tracks synchronized with video, action class labels, RGB video frames (egocentric), depth maps (2D arrays with metric distances), audio waveforms (mono or stereo), 3D skeletal joint positions (temporal sequences), camera intrinsics and extrinsics, egocentric RGB video of human performing task, human skeletal motion capture (3D joint positions), task labels and phase boundaries, object pose or scene state annotations, gripper/hand state (open/closed, contact), RGB image frames from egocentric video, natural language captions (English text), temporal frame indices or timestamps, action class or task phase labels, depth maps (metric or normalized scale), camera intrinsics and distortion parameters, optical flow or motion estimates, temporal frame sequences, egocentric RGB video frames, depth maps and 3D point clouds, audio waveforms, 3D skeletal joint positions, object pose and scene state annotations

Produces: video frames (image sequences), 3D skeletal pose sequences, action classification labels, temporal segmentation boundaries, 3D point clouds or mesh reconstructions, depth predictions or completion masks, audio-visual correspondence labels, temporal flow or motion vectors, scene segmentation masks, robot joint angle trajectories, end-effector pose sequences, gripper command signals, action phase labels, success/failure outcome labels, caption embeddings (vector representations), image-caption similarity scores, generated captions (text), temporal alignment labels (word-to-frame mappings), predicted depth maps, depth uncertainty/confidence estimates, depth completion masks, temporal consistency metrics, 3D point clouds from predicted depth, action sequences (joint angles, end-effector poses, or discrete actions), task success/failure labels, predicted next observations (world model outputs), attention maps or saliency indicating action-relevant regions, task phase predictions

UnfragileRank

Adoption15%(35% weight)

Quality14%(25% weight)

Ecosystem60%(20% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

6 capabilities

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Data Sources

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Capabilities6 decomposed

egocentric video-action dataset sampling with first-person perspective alignment

Medium confidence

Solves for

Best for

Robotics researchers training imitation learning models from human demonstrations

Computer vision teams building egocentric action recognition for AR/VR headsets

Embodied AI researchers developing agents that learn from first-person video

Requires

HuggingFace datasets library (transformers>=4.0)

Video codec support for H.264/H.265 decoding (ffmpeg or similar)

Minimum 500GB disk space for full dataset (14.56M downloads suggests multi-GB total size)

Limitations

Dataset is English-language region-locked (US), limiting cross-cultural action recognition generalization

Egocentric perspective introduces domain gap when transferring to third-person or robot camera geometries

Motion capture data may not align perfectly with all video frames due to occlusion or marker dropout in original recordings

What makes it unique

vs alternatives

multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams

Medium confidence

Solves for

Best for

3D computer vision researchers building egocentric SLAM or visual odometry systems

Multimodal AI teams training fusion models that combine video, depth, and audio

Robotics engineers developing perception systems for embodied agents in real-world environments

Requires

Libraries for 3D data handling (Open3D, trimesh, or pytorch3d)

Depth map decoders (OpenEXR, PNG 16-bit, or custom formats)

Audio processing library (librosa, scipy.io.wavfile)

Limitations

Depth data quality varies with sensor type (RGB-D vs LiDAR) and may have holes/noise in reflective or transparent surfaces

Audio-visual synchronization assumes fixed hardware latency; cross-device recordings may have temporal drift

3D/4D annotations require dense labeling, so dataset may have sparse temporal coverage or limited spatial resolution in some sequences

What makes it unique

vs alternatives

More comprehensive than ScanNet or Replica for embodied AI because it captures dynamic scenes with audio and motion, not just static 3D geometry

robotics manipulation task dataset with human demonstration video-to-action mapping

Medium confidence

Solves for

Best for

Robotics teams implementing learning from demonstration (LfD) for manipulation tasks

Embodied AI researchers training visuomotor policies from human video

Companies building robot learning systems for industrial or household automation

Requires

Robot kinematics library (PyBullet, MuJoCo, or robot-specific SDK)

Motion capture parsing tools for converting skeletal data to joint angles

Video processing pipeline (OpenCV, ffmpeg) for frame extraction and synchronization

Limitations

Human hand morphology differs from robot grippers, requiring domain adaptation or explicit hand-to-gripper mapping

Egocentric perspective from human eye level may not transfer directly to robot camera mounting heights or field-of-view constraints

Action labels are discrete or low-frequency, so high-frequency robot control (>100Hz) requires interpolation or learned upsampling

What makes it unique

vs alternatives

image-to-text captioning dataset with egocentric context and temporal grounding

Medium confidence

Solves for

Best for

NLP teams building vision-language models for egocentric understanding

AR/VR developers creating assistive systems that narrate or explain first-person experiences

Multimodal AI researchers training models that understand embodied action semantics

Requires

Vision-language model framework (transformers, CLIP, or similar)

Text tokenizer compatible with caption vocabulary

Image loading library (PIL, OpenCV)

Limitations

Captions are English-only (US region), limiting multilingual vision-language model training

Temporal grounding may be coarse (frame-level or clip-level) rather than fine-grained (word-to-frame alignment)

Egocentric perspective introduces bias toward hand-centric and action-centric descriptions, potentially underrepresenting background or passive observation

What makes it unique

Captions are grounded in egocentric first-person perspective with temporal sequence context, rather than generic object descriptions — enables models to learn action intent and embodied semantics

vs alternatives

depth estimation training dataset with egocentric multi-view and temporal consistency constraints

Medium confidence

Solves for

Best for

Computer vision researchers training depth estimation models for egocentric/first-person applications

AR/VR teams building real-time depth sensing for mobile devices with limited hardware

Robotics engineers developing visual odometry and SLAM systems from monocular egocentric input

Requires

Depth map processing library (OpenCV, scipy, or custom loaders)

Optical flow or scene flow computation (FlowNet, RAFT, or similar)

Video synchronization tools to align RGB and depth streams

Limitations

Depth ground truth may be sparse or noisy depending on sensor (RGB-D cameras have limited range; LiDAR has sparse coverage)

Egocentric camera motion is constrained by human head/body kinematics, limiting diversity of viewpoint changes compared to arbitrary camera trajectories

Temporal consistency assumptions break down during rapid head motion, occlusion, or dynamic scene changes

What makes it unique

vs alternatives

More suitable for egocentric depth learning than NYU Depth or ScanNet because it captures first-person perspective and dynamic scenes; more temporally structured than single-frame depth datasets

embodied ai agent training dataset with multimodal observation-action pairs and task structure

Medium confidence

Solves for

Best for

Embodied AI researchers training agents for household or industrial robotics tasks

Multimodal learning teams building foundation models for embodied understanding

Companies developing autonomous systems that learn from human demonstrations

Requires

Embodied AI framework (Habitat, SAPIEN, or custom environment simulator)

Multimodal data loader supporting video, depth, audio, and skeletal data

Task graph or state machine representation for encoding task structure

Limitations

Action space is constrained to human-executable actions; robot-specific actions (high-frequency joint control) require post-processing or learned mapping

Task diversity may be limited to specific domains (e.g., kitchen manipulation, office navigation), reducing generalization to novel tasks

Observation-action alignment assumes synchronized recording; latency or asynchronous data collection introduces temporal misalignment

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

xperience-10m

Capabilities6 decomposed

egocentric video-action dataset sampling with first-person perspective alignment

multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams

robotics manipulation task dataset with human demonstration video-to-action mapping

image-to-text captioning dataset with egocentric context and temporal grounding

depth estimation training dataset with egocentric multi-view and temporal consistency constraints

embodied ai agent training dataset with multimodal observation-action pairs and task structure

Related Artifactssharing capabilities

droid_1.0.1

PhysicalAI-Robotics-GR00T-X-Embodiment-Sim

mdm_depth

Holovolo

LivePortrait

11-877: Advanced Topics in MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to xperience-10m

Are you the builder of xperience-10m?

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Data Sources

xperience-10m

Capabilities6 decomposed

egocentric video-action dataset sampling with first-person perspective alignment

multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams

robotics manipulation task dataset with human demonstration video-to-action mapping

image-to-text captioning dataset with egocentric context and temporal grounding

depth estimation training dataset with egocentric multi-view and temporal consistency constraints

embodied ai agent training dataset with multimodal observation-action pairs and task structure

Related Artifactssharing capabilities

droid_1.0.1

PhysicalAI-Robotics-GR00T-X-Embodiment-Sim

mdm_depth

Holovolo

LivePortrait

11-877: Advanced Topics in MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to xperience-10m

Are you the builder of xperience-10m?

Get the weekly brief

Data Sources