Update model card.

README.md

@@ -13,15 +13,16 @@ pipeline_tag: text-to-motion
 # KV-Control (T-Concat v4 backbone)
 
 Sparse-keyframe, multi-joint controllable text-to-motion generation. The
-repository at [github.com/Tevior/KV-Control](https://github.com/Tevior/KV-Control)
+repository at [github.com/CHDTevior/KV-Control](https://github.com/CHDTevior/KV-Control)
 contains the full training and inference code.
 
 ## What is here
 
 | Path | Content | Size |
 |---|---|---|
-| `base_t_concat_v4/model/net_best_fid.tar` | Pre-trained T-Concat v4 masked-transformer base (the paper main backbone) | 168 MB |
-| `kv_control/model/net_best_kps.tar`       | KV-Control adapter trained on the base above | 520 MB |
+| `base_t_concat_v4/model/net_best_fid.tar` | Pre-trained T-Concat v4 masked-transformer base (the paper main backbone, Ep 400) | 168 MB |
+| `kv_control/model/net_best_top3.tar`      | **Cross multi-joint** KV-Control adapter — paper Tab 4 multi-joint block (`net_best_top3` @ Ep 6000, control=cross) | 520 MB |
+| `kv_control_trajectory/model/net_best_kps.tar` | **Single-joint pelvis** KV-Control adapter — paper Tab 4 headline row (`net_best_kps` @ Ep 6000, control=trajectory) | 520 MB |
 | `vqvae/net_best_fid.pth`                  | Part-aware VQ-VAE tokenizer (128 codes × 6 parts) | 236 MB |
 | `vqvae/skeleton_partition.json`           | Skeleton partition for the part-aware VQ | 1 KB |
 | `stats/{mean,std}.npy`                    | Normalization stats matching the released VQ | 4 KB |
@@ -35,13 +36,33 @@ contains the full training and inference code.
 ## How to use
 
 ```bash
-git clone https://github.com/Tevior/KV-Control.git
+git clone https://github.com/CHDTevior/KV-Control.git
 cd KV-Control
 bash scripts/download_checkpoints.sh   # populates checkpoints/, aux/ → glove/, body_models/
 ```
 
 Refer to the GitHub README for installation and quick-start commands.
 
+## Checkpoint provenance & expected metrics
+
+Both released KV-Control adapters are evaluated with the paper **M3 hybrid**
+protocol on the HumanML3D `test` split (Stage-1 dynamic TTT `each_iter=35
+--ttt_dynamic` T=10; Stage-2 600-step embedding opt; `cfg=3.25`,
+`--cond_drop_prob 0.0 --pred_num_batch 16 --seed 3407`):
+
+| Checkpoint | `--control` | Paper row | Expected (5r mean) |
+|---|---|---|---|
+| `kv_control/model/net_best_top3.tar` | `cross` | Tab 4 multi-joint | KPS ≈ **0.80 cm** (best 0.71) |
+| `kv_control_trajectory/model/net_best_kps.tar` | `trajectory` | Tab 4 headline | KPS ≈ **0.40 cm**, FID ≈ 0.065, Top-3 ≈ 0.799 |
+
+The single-joint pelvis row is the paper headline; the cross checkpoint is the
+multi-joint result. They come from two separate fine-tuning runs (pelvis vs
+cross), both on the same frozen `base_t_concat_v4` backbone. See the GitHub
+README §3 for the exact reproduction commands. `scripts/sanity_check_equivalence.py`
+regenerates one designed trajectory and reports KPS (≈ 1.7 cm on that
+hand-crafted 6-joint sample); it is an install smoke test, **not** a benchmark
+or an external-reference diff.
+
 ## Licenses
 
 * Our weights (`base_t_concat_v4`, `kv_control`, `vqvae`, `stats`) — **MIT**.