Enabling Versatile Controls for Video Diffusion Models
This work addresses the problem of enabling versatile controls for video generation, which is incremental as it builds on existing video diffusion models to improve controllability.
The paper tackles the challenge of achieving precise and flexible control over fine-grained spatiotemporal attributes in video generation by introducing VCtrl, a framework that integrates diverse user-specified control signals into pre-trained video diffusion models, enhancing controllability and generation quality as demonstrated through comprehensive experiments and human evaluations.
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.