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README.md

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+  <img src="assets/hugging_face_gif_01_small.gif" alt="SDG-SynHuman Preview" width="100%">
+</p>
+
+## Dataset Description: <br>
+
+The SDG-SynHuman is a large-scale synthetic video dataset of digital humans rendered in diverse indoor and outdoor 3D environments. The dataset contains 236,937 clips, totaling approximately 5,841 hours of video, and is designed to support training and post-training of NVIDIA Cosmos world foundation models and related physical AI research.
+
+Each sample is a temporally coherent 60-120 second video clip rendered at 1080p and 30 fps. Clips contain multiple digital humans performing animation sequences in a sampled 3D environment with a controlled camera trajectory. The dataset spans 4,050 digital human assets, 8,184 unique animations, 198 indoor environments, 200 outdoor city environments, and 14 camera-motion scenarios, providing broad variation in human appearance, motion, scene context, lighting, and camera behavior.
+
+The dataset is intended as a controllable synthetic supplement to real-world human video data for applications such as world-model pretraining and post-training, camera-motion generalization, depth and geometry-aware learning, human-scene interaction modeling, and physical AI research.
+
+This dataset is ready for commercial/non-commercial use.
+
+## Dataset Owner(s): <br>
+
+NVIDIA Corporation
+
+## Dataset Creation Date: <br>
+
+Creation Date: 2026-04-28
+
+## License/Terms of Use: <br>
+
+This dataset is available for both commercial and non-commercial use.
+
+[Visit the NVIDIA Legal Release Process](https://nvidia.sharepoint.com/sites/ProductLegalSupport) for instructions on getting legal support for a license selection:
+
+## Intended Usage: <br>
+
+SynHuman is intended for AI/ML researchers and developers working on world foundation models, video generative models, human-centric video understanding, camera-control modeling, and physical AI.
+
+The dataset is designed to serve as a controllable synthetic supplement to real-world human video data, especially for tasks that benefit from deterministic camera and geometry supervision.
+
+Typical use cases include:
+
+- pretraining and post-training video world models
+- training or evaluating camera-motion generalization
+- depth- and geometry-aware learning with paired RGB, metric depth, and camera calibration
+- human-scene interaction modeling
+- studying the role of synthetic data in physical AI systems
+
+This dataset is not intended to replace real-world validation. Models trained with SDG-SynHuman should be evaluated on representative real-world data before use in production systems.
+
+It should not be used for biometric identification, real-person recognition, surveillance targeting, or inferring sensitive attributes of real individuals.
+
+## Dataset Characterization <br>
+
+** Data Collection Method<br>
+
+* [Synthetic] - [SDG-SynHuman is a fully synthetic dataset generated through NVIDIA’s Synthetic Data Generation pipeline. Scenarios are procedurally sampled from structured world configurations that define the environment, lighting, digital humans, animations, camera model, and camera trajectory. Each clip is rendered in a 3D simulation environment using synthetic digital humans and synthetic or simulation-ready scene assets, including internal NVIDIA environments, indoor environments adapted from the SceneSmith example-scenes dataset, and outdoor city environments generated with Blender’s CityGenerator plugin. No real-world video footage, real human-subject data, or real-world audio is used.] <br>
+
+** Labeling Method<br>
+
+* [Synthetic] - [All annotations are synthetic and generated deterministically from the underlying USD scene graph during simulation. The release includes RGB video, metric depth, per-frame camera intrinsics and extrinsics, world configuration metadata, and asset-level metadata describing the environment, digital humans, animation assignments, placements, and camera behavior. No human labeling is involved.] <br>
+
+## Dataset Format <br>
+This dataset is delivered in 1,215 tar shards found in \path{shards/}. Each tar bundles 200 samples. Each sample is identified by its UUID.
+
+Dataset Format: Video with structured annotations.
+
+Clip duration: Each clip is approximately 60-120 seconds long.
+
+Packaging: Each sample is associated with a UUID and includes rendered RGB video, depth output, per-frame camera parameters, and scene metadata.
+
+Per-sample files include:
+
+- `video/uuid.mp4` - RGB video rendered at 1080p and encoded as H.264.
+- `depth/uuid.mkv` - Metric depth rendered at 1080p as distance to the image plane, encoded losslessly with FFV1.
+- `depth/uuid_depth.json` - Companion depth metadata, including depth range, resolution, frame rate, and data type for metric reconstruction.
+- `meta/uuid_camera.json` - Per-frame camera intrinsics and extrinsics.
+- `meta/uuid.json` - Scene configuration, including environment, lighting, digital-human agents, camera setup, and animation task definitions.
+- `description/uuid.json` - Asset metadata, including spawned asset inventory, digital humans, motions, placements, and provenance information.
+
+## Dataset Quantification <br>
+
+Record Count: 236,937 records, totaling approximately 5,841 hours of video. <br>
+
+Feature Count (per record): RGB and depth videos, depth companion metadata (depth range, resolution, frame rate, and dtype), per-frame camera intrinsics and extrinsics, world configuration metadata, and asset configuration metadata. <br>
+
+Total Data Storage: Approximately 80TB (exact size TBD upon final HuggingFace upload). <br>
+
+## Reference(s): <br>
+
+HuggingFace:  
+https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-SynHuman
+
+Upstream environment asset reference:  
+https://huggingface.co/datasets/nepfaff/scenesmith-example-scenes
+
+Blender CityGenerator plugin (version 2.4):  
+https://superhivemarket.com/products/the-city-generator
+
+Related announcement — NVIDIA Physical AI Dataset:  
+https://blogs.nvidia.com/blog/open-physical-ai-dataset/
+
+## Ethical Considerations: <br>
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.