Prepare Motion Data

Prepare Motion Data

Turn user motion into validated HoloMotion training data:

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:

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.