Enabling Versatile Controls for Video Diffusion Models

• URL: http://arxiv.org/abs/2503.16983v1
• Date: Fri, 21 Mar 2025 09:48:00 GMT
• Title: Enabling Versatile Controls for Video Diffusion Models
• Authors: Xu Zhang, Hao Zhou, Haoming Qin, Xiaobin Lu, Jiaxing Yan, Guanzhong Wang, Zeyu Chen, Yi Liu,
• Abstract summary: VCtrl is a novel framework designed to enable fine control over pre-trained video diffusion models.<n> Comprehensive experiments and human evaluations demonstrate VCtrl effectively enhances controllability and generation quality.
• Score: 18.131652071161266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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