nvidia-isaac/video_to_data

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# Video to Data (V2D) > An end-to-end pipeline that converts human demonstration videos into simulation-ready assets and physics-grounded robot training data. **[Documentation](https://nvidia-isaac.github.io/video_to_data/)** · **[Datasets (Hugging Face)](https://huggingface.co/collections/nvidia/video-to-data)** · **[Robotic Grounding Project Page](https://nvidia-isaac.github.io/video_to_data/chord/)** · **[Robotic Grounding Tech Report](https://nvidia-isaac.github.io/video_to_data/chord/chord.pdf)** ![Video to Data pipeline — from human demonstration video through ingestion, reconstruction, and robotic grounding in Isaac Lab to a physics-grounded policy, dataset, and real-robot deployment](docs/figures/v2d_overview.png) --- ## Contents - [Overview](#overview) - [E2E workflow](#e2e-workflow) - [Packages](#packages) - [Prerequisites](#prerequisites) - [Quickstart](#quickstart) - [Video Ingestion Agent](#video-ingestion-agent-video--queryable-action-database) - [Reconstruction](#reconstruction-video--3d-data) - [Robotic Grounding](#robotic-grounding-data--rl-policy) - [FlashCHORD accelerated training](robotic_grounding/flash_chord/README.md) - [Design philosophy](#design-philosophy) - [Contributing](#contributing) --- ## Overview Video to Data (V2D) turns raw human demonstrations into robot-ready training data through three composable stages and a repository-root E2E workflow. Each stage runs independently and writes its artifacts to disk, so you can stop, inspect, cache, and recompose the pipeline at any boundary. 1. **Video Ingestion Agent** — a LangGraph-driven agentic workflow that segments demonstration videos into temporally-bounded action clips, extracts an entity-relation scene graph, and stores per-frame SigLIP-2 embeddings. The result is a queryable action database (`graph.db` + `vector.db`) that lets downstream stages select which clips to process via natural-language retrieval, instead of brute-forcing the full video. 2. **Reconstruction** — containerized vision modules turn the selected RGB (or stereo) clips into per-frame depth, object masks, textured meshes, 6-DoF object poses, and SMPL human body parameters. Multi-view pipelines (`run_mv_hoi_reconstruction`, `run_mv_calibration`) orchestrate the full reconstruction from a rosbag. 3. **Robotic Grounding** — human motion (e.g. Arctic) is retargeted onto the target robot embodiment (Sharpa), then the reconstructed scene and retargeted motion drive Isaac Lab environments trained with RSL-RL PPO to produce deployable policies. ## E2E workflow An end-to-end workflow serves as a reference for connecting the repository's components into a complete reconstruction-to-policy pipeline. The included example starts with a raw egocentric human demonstration captured on an iPhone and continues through reconstruction, retargeting, source-expert training, demonstration collection, GR00T N1.7 fine-tuning, and closed-loop evaluation. It has been verified with both floating Sharpa and Vega Sharpa embodiments. `run_e2e.sh` provides a reproducible launcher that orchestrates the stages and records their state for inspection and resumption. A companion agentic skill uses the launcher under the hood to guide setup and execution. The main path trains its own source expert; the bundled tissue-box ONNX policy is an optional shortcut for users who want to start directly with GR00T fine-tuning. The complete workflow can run on a single NVIDIA RTX A6000 GPU with 48 GB of VRAM and has also been verified on NVIDIA L40 and L40S GPUs. Follow the [E2E workflow guide](docs/e2e_workflow.md) for the runnable tissue-box demo, setup requirements, acceptance gates, and resume behavior. ## Packages | Package | Role | Runtime | |---|---|---| | [`video_ingestion_agent/`](video_ingestion_agent/) | Video → action segments + entity scene graph + frame embeddings. LangGraph pipeline (segment → verify/refine → entity graph → embeddings) plus an EGAgent-style natural-language retrieval agent and an optional Gradio UI. | Python venv + vLLM server | | [`reconstruction/`](reconstruction/) | Video → depth, masks, meshes, 6D poses, human body. 18 containerized modules + multi-view pipelines. | Docker (per-module images) | | [`robotic_grounding/`](robotic_grounding/) | RL training on NVIDIA Isaac Lab 2.3.1 with RSL-RL (PPO); motion retargeting utilities. | Docker (`nvcr.io/your-org/your-team`) | | [`robotic_grounding/flash_chord/`](robotic_grounding/flash_chord/) | Accelerated reference-tracking training with PPO and FlashSAC; [setup and complete example workflow](robotic_grounding/flash_chord/README.md#setup). | Separate Newton / Warp / JAX container | | [`groot_finetune/`](robotic_grounding/groot_finetune/) | Expert demonstration collection, LeRobot dataset conversion, GR00T N1.7 fine-tuning, and closed-loop evaluation. | Python 3.10 + external Isaac-GR00T checkout | ## Prerequisites - Docker with GPU support ([install](https://docs.docker.com/engine/install/ubuntu/)) - [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) - Python 3.10+ - NVIDIA driver 580.126.09 / CUDA 13.0 recommended (for `robotic_grounding`) The separate v0.2 multi-view HOI object-reconstruction pipeline is validated on RTX A6000 (SM 86) and L40S (SM 89). Blackwell GPUs with compute capability 12.0 (`sm_120`), including RTX PRO 6000 Blackwell, are not supported by the TensorRT and cuVSLAM versions in `v2d_cusfm`. The reconstruction container build and pipeline launcher check this before starting; see the [HOI GPU compatibility notes](reconstruction/modules/v2d_hoi_object_reconstruction/README.md#gpu-compatibility). ## Quickstart ### Video Ingestion Agent (video → queryable action database) ```bash cd video_ingestion_agent uv venv .venv && source .venv/bin/activate uv pip install -e ".[all]" # vLLM, webapp, benchmark, dev tools # 1. Start the vLLM server (loads the VLM, ~1 minute) python scripts/serve.py -c configs/ingestion.yaml # 2. Ingest a video — segmentation → entity graph → report python scripts/run_ingestion.py path/to/video.mp4 \ -c configs/ingestion.yaml --no-verify -o runs/my_run # 3. Retrieve clips with natural language python scripts/run_retrieval.py "Find clips where someone picks up a mug" \ -d outputs/ -c configs/retrieval.yaml # 4. Or browse interactively in the web UI python scripts/run_webapp.py ``` See [video_ingestion_agent/README.md](video_ingestion_agent/README.md) for hardware requirements, the full extras list, the verify/refine loop, and batch-ingestion across multiple GPUs. Pre-publication TODOs are tracked in [video_ingestion_agent/docs/release_readiness.md](video_ingestion_agent/docs/release_readiness.md). ### Reconstruction (video → 3D data) ```bash cd reconstruction # Install host-side orchestration wrappers (lightweight, no model dependencies) ./scripts/install_pacakages.sh # Build per-module Docker images ./scripts/build_containers.sh # Run a minimal video→depth example (MoGe) python -m v2d.moge.docker.run_download_weights --output_dir data/weights/moge python -m v2d.moge.docker.run_video_to_depth \ --video_path modules/v2d_moge/assets/test_video.mp4 \ --depth_folder data/outputs/moge/depth \ --intrinsics_folder data/outputs/moge/intrinsics \ --weights_path data/weights/moge ``` Full multi-view HOI pipeline (rosbag → textured object mesh + SMPL body): ```bash python -m v2d.pipelines.run_mv_hoi_reconstruction \ --rosbag_path /data/rosbags/session1 \ --output_dir /data/datasets/session1 \ --calibration_camera_params_path /data/datasets/calibration/extrinsics/edex \ --obj_mesh_path /data/meshes/object.glb ``` See [reconstruction/README.md](reconstruction/README.md) for the complete module reference, including [Grounding DINO](reconstruction/README.md#v2d_grounding_dino), [SAM2](reconstruction/README.md#v2d_sam2), [FoundationPose](reconstruction/README.md#v2d_foundation_pose), [SAM3D-Body](reconstruction/README.md#v2d_sam3d_body), and others. ### Robotic Grounding (data → RL policy) **Accelerated training:** use [FlashCHORD's setup and workflow](robotic_grounding/flash_chord/README.md#setup) for Newton/Warp/JAX training on the included Sharpa and G1 examples. FlashCHORD has its own container and dependencies. **Quick start:** the from-scratch setup & run guide is [robotic_grounding/docs/SETUP.md](robotic_grounding/docs/SETUP.md) — it covers the two Docker images, downloading each dataset from its original public source, the directory layout, and how to run the full hand→robot retargeting pipeline. Throughout, `<HMD>` (human-motion-data root) is a directory you choose — e.g. `~/datasets/human_motion_data` — that holds `mano/` and one subdirectory per dataset (`taco/`, `hot3d/`, …); see [docs/SETUP.md §4](robotic_grounding/docs/SETUP.md). ```bash cd robotic_grounding # One-time host setup (git-lfs, pre-commit) + robot assets (LFS) bash workflow/setup_deps.sh git lfs pull # Build both pipeline images (loader + robotic-grounding) in one shot python scripts/run_pipeline_docker.py --build-only # Run the full pipeline on a dataset (download it first per docs/SETUP.md §6). # <HMD> is the data root holding mano/ and each <dataset>/. python scripts/run_pipeline_docker.py taco \ --hmd <HMD> --mano-dir <HMD>/mano --max-sequences 2 # small smoke test ``` Reproduce the sequences end-to-end (arctic / hot3d / taco) in a self-contained workspace — see [robotic_grounding/docs/EXAMPLE_SEQUENCES.md](robotic_grounding/docs/EXAMPLE_SEQUENCES.md) for the sequence list and prerequisites: ```bash HMD=<HMD> ./run_example_sequences.sh # → RL-ready parquets under <HMD>/example_sequences/<ds>/<ds>_processed/ ``` Then enter the Isaac Lab container and train a policy on the retargeted motion: ```bash ./workflow/run.sh build ./workflow/run.sh start [version] [gpu_id] # build + enter the container python scripts/rsl_rl/train.py --task Sharpa-V2D-v0 # inside the container ``` See [robotic_grounding/README.md](robotic_grounding/README.md) for retargeting, debug environments, and task definitions. ### Visualizer (retargeting gallery) Browse retargeted sequences as interactive 3D animations in your browser. See [robotic_grounding/README.md#visualizer](robotic_grounding/README.md#visualizer) for setup instructions. ## Design philosophy - **Host orchestration, containerized inference.** The host runs thin Python wrappers that `docker run` each module; all model dependencies live inside their respective images. No CUDA or PyTorch is ever installed on the host. - **Typed contracts between packages.** Modules communicate through strongly-typed dataclasses in [`v2d_common`](reconstruction/modules/v2d_common/) (`DepthImage`, `CameraIntrinsics`, `Transform3d`, `BoundingBox`, `Mask`) — never raw arrays across package boundaries. - **File-based dataflow.** Modules write intermediate artifacts to disk (depth PNGs, pose JSONs, mask PNGs, etc.), enabling independent execution, caching, and pipeline composition via [`v2d_pipelines`](reconstruction/modules/v2d_pipelines/). ## Contributing See the contributing guide in [reconstruction/README.md](reconstruction/README.md#contributing) for adding new reconstruction modules. Each new module must expose a Docker image, a `run_download_weights` entry point (if weights are required), a `run_shell` entry point, and a typed API surface consistent with `v2d_common`.

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Diagnostic checklist for the Video Ingestion Agent environment — run when something is broken, before a first ingest, or as a sanity check. Use this skill whenever the user reports the pipeline isn't working, the vLLM server won't start or crashes during…

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2026-06-23

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