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CyrusZhang312/JointControlvideo
JointControlvideo is a image-to-video model from CyrusZhang312. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Official checkpoint for "Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints" (ECCV 2026).
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Updated Jul 3, 2026
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From the Hugging Face model README
Official checkpoint for "Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints" (ECCV 2026).
Given a starting frame and a sequence of 3D MANO hand-joint trajectories, this checkpoint generates an egocentric video that follows the prescribed hand motion — conditioned via occlusion-aware, 3D geometric hand-joint embeddings injected directly into the latent space of Wan2.1-I2V-14B-480P, instead of dense 2D tracks or a separate pose tokenizer.
| File | Description |
|---|---|
dit.safetensors | LoRA weights for the Wan2.1 DiT (q,k,v,o,ffn.0,ffn.2, rank 64) + the expanded patch_embedding (+16 input channels for the fused hand embedding) |
hand-controller.safetensors | HandConditioningModule — the occlusion-aware hand-joint conditioning network (42 MANO joints, 2×21 per hand) |
Both are trained on top of a frozen Wan2.1-I2V-14B-480P backbone; you still need the base model's DiT/VAE/text-encoder/CLIP weights, which are pulled automatically from Wan-AI/Wan2.1-I2V-14B-480P the first time you run the pipeline.
Set up the code from ZhangCYG/JointControlVideo
@inproceedings{zhang2026controllable,
title = {Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints},
author = {Zhang, Chenyangguang and Ye, Botao and Chen, Boqi and Delitzas, Alexandros and Wang, Fangjinhua and Pollefeys, Marc and Wang, Xi},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2026}
}
Apache 2.0.