KV-Control / README.md
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---
license: mit
library_name: pytorch
tags:
- motion-generation
- text-to-motion
- humanml3d
- controllable-generation
- kv-control
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)
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 |
| `vqvae/net_best_fid.pth` | Part-aware VQ-VAE tokenizer (128 codes Γ— 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 |
| `clip/ViT-B-32.pt` | OpenAI CLIP ViT-B/32 visual + text encoder | 336 MB |
| `t2m/Comp_v6_KLD005/opt.txt + meta/` | Frozen evaluation encoder config & stats | 3 KB |
| `t2m/text_mot_match/model/finest.tar` | Pre-trained text-motion eval encoder (Guo et al., 2022) | 235 MB |
| `t2m/length_estimator/model/finest.tar` | Pre-trained motion-length predictor | 1.7 MB |
| `aux/body_models/` | SMPL neutral mesh + face / J_regressor (SMPL license) | 234 MB |
| `aux/glove/` | Vocab files for the length estimator | 10 MB |
## How to use
```bash
git clone https://github.com/Tevior/KV-Control.git
cd KV-Control
bash scripts/download_checkpoints.sh # populates checkpoints/, aux/ β†’ glove/, body_models/
```
Refer to the GitHub README for installation and quick-start commands.
## Licenses
* Our weights (`base_t_concat_v4`, `kv_control`, `vqvae`, `stats`) β€” **MIT**.
* CLIP ViT-B/32 β€” released by OpenAI under MIT.
* SMPL body model under `aux/body_models/` β€” original SMPL license (research-only).
* Text-motion eval encoder / length estimator under `t2m/` β€” re-distributed
from the HumanML3D / Guo et al. 2022 release for reproducibility.
## Citation
```bibtex
@article{kvcontrol2026,
title = {KV-Control: Sparse-Keyframe Multi-Joint Text-to-Motion Generation},
author = {... (under review) ...},
year = {2026},
}
```