Enabling Versatile Controls for Video Diffusion Models

Xu Zhang, Hao Zhou, Haoming Qin, xiaobin Lu, yanjiaxing, wang guanzhong, Zeyu Chen, Yi Liu

Enabling Versatile Controls for Video Diffusion Models: 15 upvotes on Hugging Face Daily Papers, #10 of 22 papers on 2025-03-24. Day-by-day upvote history.

Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research. To address these limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework designed to enable fine-grained control over pre-trained video diffusion models in a unified manner. VCtrl integrates diverse user-specified control signals-such as Canny edges, segmentation masks, and human keypoints-into pretrained video diffusion models via a generalizable conditional module capable of uniformly encoding multiple types of auxiliary signals without modifying the underlying generator. Additionally, we design a unified control signal encoding pipeline and a sparse residual connection mechanism to efficiently incorporate control representations. Comprehensive experiments and human evaluations demonstrate that VCtrl effectively enhances controllability and generation quality. The source code and pre-trained models are publicly available and implemented using the PaddlePaddle framework at http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.

Paper page on Hugging Face · arXiv

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