prepare-motion-data

# Prepare Motion Data Turn user motion into validated HoloMotion training data: ```text raw source -> canonical HoloSMPL -> formal HoloSMPL -> robot HDF5 ``` Read `holosmpl/README.md` and `docs/motion_retargeting.md` before choosing commands. Use `docs/holomotion_motion_file_spec.md` when producing or inspecting deployment/evaluation NPZ files. ## Determine the input contract 1. Identify the source format, coordinate frame, units, frame rate, skeleton, joint order, and whether shape parameters are available. 2. Run `python -m holosmpl list-sources` and inspect the matching README under `holosmpl/supported_datasets/` or `holosmpl/supported_devices/`. 3. Do not describe an arbitrary format as supported merely because it resembles SMPL, BVH, or another registered source. 4. For an unsupported format, inspect representative files and add a source adapter following the `Adding a Source` section in `holosmpl/README.md`. Keep raw user data outside Git. Do not commit motion datasets, generated HDF5/NPZ files, licensed body models, or personal paths. ## Convert and validate For a registered source, prefer the end-to-end entry point: ```bash python -m holosmpl convert \ --source <source_name> \ --input-root <raw_root> \ --output-root <output_root> ``` Use the source-specific README for required options. Start with a small representative subset before converting a complete dataset. Validate the canonical result before retargeting: - Z-up world frame and meter scale; - expected pose layout and body orientation; - stable root translation and floor/contact behavior; - correct 50 Hz output; - finite arrays, consistent frame counts, and useful provenance metadata. Use HoloSMPL visualization or rendering commands from `holosmpl/README.md`. Do not accept schema validity as proof that the motion semantics are correct. ## Retarget for training Use the formal HoloSMPL H5 output as the HoloRetarget input. Follow `docs/motion_retargeting.md` to produce robot HDF5. HoloRetarget requires a Newton/Warp runtime with a visible CUDA device. Use the project training environment and validate a small shard before scaling. Check that the resulting robot data has the documented reference arrays, 29-DOF ordering for the supported G1 pipeline, finite values, plausible joint limits, and correct clip metadata. ## Completion Report: - source type and adapter used; - input assumptions; - output roots and formats; - schema and visual checks performed; - rejected or suspicious clips; - whether robot HDF5 was produced; - remaining unsupported semantics. Do not claim the dataset is training-ready until both human-motion validation and robot-retarget validation pass.