{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-ropedia-ai--xperience-10m","slug":"ropedia-ai--xperience-10m","name":"xperience-10m","type":"dataset","url":"https://huggingface.co/datasets/ropedia-ai/xperience-10m","page_url":"https://unfragile.ai/ropedia-ai--xperience-10m","categories":["model-training"],"tags":["task_categories:video-classification","task_categories:image-to-text","task_categories:depth-estimation","task_categories:robotics","language:en","license:other","size_categories:1M<n<10M","modality:3d","modality:audio","modality:video","region:us","egocentric","first-person","multimodal","3d","4d","embodied-ai","robotics","human-motion","mocap"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_0","uri":"capability://data.processing.analysis.egocentric.video.action.dataset.sampling.with.first.person.perspective.alignment","name":"egocentric video-action dataset sampling with first-person perspective alignment","description":"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.","intents":["Train video classification models that recognize actions from egocentric (first-person) camera perspectives for AR/VR applications","Build embodied AI agents that understand human actions by learning from first-person video paired with motion capture ground truth","Develop 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"],"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"],"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)","Python 3.8+ for dataset loading and preprocessing"],"input_types":["video files (egocentric first-person perspective)","motion capture skeletal data (3D joint positions)","audio tracks synchronized with video","action class labels"],"output_types":["video frames (image sequences)","3D skeletal pose sequences","action classification labels","temporal segmentation boundaries"],"categories":["data-processing-analysis","embodied-ai","robotics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_1","uri":"capability://data.processing.analysis.multimodal.3d.4d.scene.reconstruction.dataset.with.synchronized.audio.visual.depth.streams","name":"multimodal 3d-4d scene reconstruction dataset with synchronized audio-visual-depth streams","description":"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.","intents":["Train 3D scene understanding models that reconstruct environments from egocentric multi-sensor input","Build audio-visual models that correlate sound sources with visual and spatial information in embodied contexts","Develop 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"],"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"],"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)","GPU with 8GB+ VRAM for loading multimodal batches"],"input_types":["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"],"output_types":["3D point clouds or mesh reconstructions","depth predictions or completion masks","audio-visual correspondence labels","temporal flow or motion vectors","scene segmentation masks"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_2","uri":"capability://data.processing.analysis.robotics.manipulation.task.dataset.with.human.demonstration.video.to.action.mapping","name":"robotics manipulation task dataset with human demonstration video-to-action mapping","description":"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.","intents":["Train behavior cloning models that map egocentric video observations to robot joint commands or end-effector trajectories","Build imitation learning systems that learn manipulation skills from human demonstrations without explicit reward engineering","Develop 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"],"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","Dataset captures only successful demonstrations; failure modes and recovery strategies are underrepresented"],"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","Python 3.8+ with numpy, scipy for trajectory processing"],"input_types":["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)"],"output_types":["robot joint angle trajectories","end-effector pose sequences","gripper command signals","action phase labels","success/failure outcome labels"],"categories":["data-processing-analysis","robotics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_3","uri":"capability://text.generation.language.image.to.text.captioning.dataset.with.egocentric.context.and.temporal.grounding","name":"image-to-text captioning dataset with egocentric context and temporal grounding","description":"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.","intents":["Train image captioning models that generate first-person action descriptions ('I am reaching for the cup') rather than third-person object lists","Build vision-language models for egocentric AR/VR applications that understand user intent from visual context","Develop 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"],"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"],"requires":["Vision-language model framework (transformers, CLIP, or similar)","Text tokenizer compatible with caption vocabulary","Image loading library (PIL, OpenCV)","Python 3.8+ with torch or tensorflow"],"input_types":["RGB image frames from egocentric video","natural language captions (English text)","temporal frame indices or timestamps","action class or task phase labels"],"output_types":["caption embeddings (vector representations)","image-caption similarity scores","generated captions (text)","temporal alignment labels (word-to-frame mappings)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_4","uri":"capability://data.processing.analysis.depth.estimation.training.dataset.with.egocentric.multi.view.and.temporal.consistency.constraints","name":"depth estimation training dataset with egocentric multi-view and temporal consistency constraints","description":"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.","intents":["Train monocular depth estimation models using egocentric video with dense depth supervision","Build self-supervised depth learning systems that exploit temporal consistency in egocentric sequences","Develop 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"],"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"],"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","GPU with 8GB+ VRAM for training depth networks"],"input_types":["RGB video frames (egocentric)","depth maps (metric or normalized scale)","camera intrinsics and distortion parameters","optical flow or motion estimates","temporal frame sequences"],"output_types":["predicted depth maps","depth uncertainty/confidence estimates","depth completion masks","temporal consistency metrics","3D point clouds from predicted depth"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-ropedia-ai--xperience-10m__cap_5","uri":"capability://data.processing.analysis.embodied.ai.agent.training.dataset.with.multimodal.observation.action.pairs.and.task.structure","name":"embodied ai agent training dataset with multimodal observation-action pairs and task structure","description":"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.","intents":["Train embodied AI agents using imitation learning from human demonstrations across diverse manipulation and navigation tasks","Build multimodal world models that predict future observations given current state and action","Develop 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"],"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","Success labels are binary or coarse-grained, limiting fine-grained reward signal for learning"],"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","Python 3.8+ with PyTorch or TensorFlow for agent training"],"input_types":["egocentric RGB video frames","depth maps and 3D point clouds","audio waveforms","3D skeletal joint positions","task labels and phase boundaries","object pose and scene state annotations"],"output_types":["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"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["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 preprocessing","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)","GPU with 8GB+ VRAM for loading multimodal batches","Robot kinematics library (PyBullet, MuJoCo, or robot-specific SDK)","Motion capture parsing tools for converting skeletal data to joint angles"],"failure_modes":["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; 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