VideoPainter - A video restoration and editing framework jointly launched by CUHK, Tencent, and other organizations.
VideoPainter is a video restoration and editing framework developed by institutions such as the Chinese University of Hong Kong, Tencent ARC Lab, the University of Tokyo, and the University of Macau. It is specifically designed for processing video content of any length. VideoPainter is based on a two-branch architecture...
What is VideoPainter?
VideoPainter is a video restoration and editing framework developed by institutions such as the Chinese University of Hong Kong, Tencent ARC Lab, the University of Tokyo, and the University of Macau. It is specifically designed for processing video content of arbitrary length. Based on a dual-branch architecture, VideoPainter combines a lightweight context encoder and a pre-trained diffusion model to achieve efficient background preservation and foreground generation. The framework supports plug-in operation, allowing users to flexibly adjust the restoration effects according to their needs. VideoPainter introduces ID resampling technology to maintain object consistency in long videos. VideoPainter has built VPData and VPBench, currently the largest video restoration datasets, containing over 390,000 video clips, supporting large-scale training and evaluation. VideoPainter demonstrates superior performance across multiple metrics, including video quality, mask region preservation, and text alignment, bringing new breakthroughs to the field of video restoration and editing.
VideoPainter's main functions
- Video repair of any lengthProcesses various content from short clips to long videos, and repairs obscured or damaged parts.
- Background Preservation and Foreground GenerationBased on a dual-branch architecture, it achieves accurate preservation of the background and high-quality generation of the foreground.
- Text-guided video editingIt supports video editing using text commands, such as adding, deleting, replacing, or modifying objects in the video.
- Maintaining object consistencyMaintain consistency in the identity of objects across long videos to avoid object drift or mutation.
- Plug-in controlIt supports combination with different diffusion models or LoRA (low-rank adaptation) models to meet diverse video generation and editing needs.
VideoPainter's technical principles
- dual-branch architecture:
- Background BranchBackground features are extracted using a lightweight context encoder and injected into a pre-trained diffusion model to ensure the coherence of the background.
- Foreground BranchBased on the generation capabilities of the diffusion model, foreground content is generated according to text prompts and combined with background features to achieve high-quality restoration.
- Lightweight context encoderThis method comprises only two layers, accounting for 6% of the main model parameters. It extracts background features and injects them into the diffusion model in a grouped manner. Based on selective feature fusion, background features are injected into the model to avoid confusion between foreground and background information.
- ID resampling technologyDuring training, the ID information of the target region is enhanced to improve the model's ability to perceive the repaired area. During inference, the repaired area features of the previous video segment are combined with the current segment to ensure the consistency of objects in the long video.
- Plug-in controlIt supports integration with different diffusion models or LoRA models, allowing users to select the appropriate model for video restoration or editing based on their needs. It is compatible with text-to-video (T2V) and image-to-video (I2V) diffusion models, further expanding its application scope.
- Large-scale dataset constructionIt automatically generates accurate segmentation masks and dense text descriptions using advanced vision models (such as SAM2, Grounding DINO, etc.). VPData and VPBen, containing over 390,000 video clips, are built to support large-scale training and evaluation.
VideoPainter's project address
- Project official website:https://yxbian23.github.io/project/video-painter/
- GitHub repository:https://github.com/TencentARC/VideoPainter
- HuggingFace model library:https://huggingface.co/TencentARC/VideoPainter
- arXiv technical paper:https://arxiv.org/pdf/2503.05639
Application scenarios of VideoPainter
- Film and television restoration and productionRepair damaged footage in old movies and TV series, or add special effects and modify scenes.
- Advertising and MarketingQuickly generate high-quality video content, add or replace backgrounds and props, and achieve personalized customization.
- Video creation and live streamingIt can fix errors or interference in videos in real time, add virtual props, and improve visual effects.
- VR and AR applicationsGenerate immersive video content, repair or modify virtual scenes, and enhance the sense of realism.
- Education and Training: Create instructional videos, restore images of ancient books, add virtual devices or annotations to enhance teaching effectiveness.