KV-Control / README.md

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metadata

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 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 × 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

git clone https://github.com/Tevior/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.

Licenses

Our weights (base_t_concat_v4, kv_control, vqvae, stats) — MIT.
CLIP ViT-B/32 — released by OpenAI under MIT.
SMPL body model under aux/body_models/ — original SMPL license (research-only).
Text-motion eval encoder / length estimator under t2m/ — re-distributed from the HumanML3D / Guo et al. 2022 release for reproducibility.

Citation

@article{kvcontrol2026,
  title  = {KV-Control: Sparse-Keyframe Multi-Joint Text-to-Motion Generation},
  author = {... (under review) ...},
  year   = {2026},
}