Point at the open data_process pipeline that turns this raw layer into UniML3D

README.md

@@ -122,6 +122,15 @@ usable = clips.filter(lambda r: r["drives_skeleton"] == "true" and r["duration_s
 Both tables are derived from each file's glTF JSON chunk by `scripts/build_dataset.py`, so they
 can be regenerated or extended without re-downloading anything.
 
+## Processing it into motion data
+
+This repository is the **raw** layer. The pipeline that turns it into canonicalized, text-paired motion clips is open at [`UniMate/data_process`](https://github.com/Friedrich-M/UniMate/tree/main/data_process): it exports each clip to NPZ, renders previews, captions them with a vision-language model, cleans the joint labels and derives the training features. The processed release built from it is [UniML3D](https://huggingface.co/datasets/Linzhan/UniML3D).
+
+```bash
+bash data_process/scripts/run_download.sh objaverse
+bash data_process/scripts/run_export.sh objaverse
+```
+
 ## Licensing and attribution
 
 The assets in `glb/` were created by third parties and **retain their individual upstream