# Official TextOp Deliverable

This folder is a compact handoff package for the current official TextOp baseline on our dataset.

Included files:

1. `mvae_ckpt_10000.pth`
   Recommended MVAE checkpoint for downstream use.
2. `dar_ckpt_50000.pth`
   Latest saved DAR checkpoint from the current training run.
3. `mvae_run.log`
   MVAE resumed training log.
4. `dar_run.log`
   DAR training log.
5. `dar_cfg.yaml`
   Exact DAR config saved by Hydra for this run.
6. `vae_src.log`
   Source path of the MVAE checkpoint cached into the DAR run.
7. `mvae_reconstruction_sample.npz`
   One exported MVAE reconstruction sample.
8. `mvae_reconstruction_summary.json`
   Reconstruction error summary for that sample batch.

Current status:

1. MVAE was trained to a late, flatter regime and stopped to free GPU for DAR.
2. DAR was trained successfully to `step 50000`.
3. DAR loss is normal and clearly lower than the early stage.

Useful numbers:

1. MVAE `loss/train_total`: `0.459224 -> 0.006279`
2. DAR `loss/train_total`: `0.498519 -> 0.100912`
3. DAR `loss/train_rec`: `0.109399 -> 0.021164`
4. DAR `loss/train_latent_rec`: `0.389046 -> 0.079731`
5. MVAE reconstruction sample mean MSE: `0.004995`
6. MVAE reconstruction sample mean MAE: `0.039733`

What "reconstruction" means here:

1. Take a real motion segment from the validation set.
2. Encode the future motion into MVAE latent.
3. Decode it back into motion using the same model.
4. Compare decoded motion against the original future motion.

So reconstruction checks whether the MVAE has learned a good motion representation.
It is not the same thing as DAR text-driven generation.