用3D高斯场+神经纹理实现100FPS的稳定人脸视频风格编辑
Portrait Video Editing Empowered by Multimodal Generative Priors
- 将人脸视频统一建模为动态3D高斯场，保证时空一致性
- 支持文本/图像多模态驱动，渲染速度超100FPS
- 适合影视特效、虚拟人生成等需要高效实时编辑的场景
我们提出PortraitGen，一种强大的人脸视频编辑方法，可实现一致且富有表现力的风格化处理。传统方法在3D和时序一致性上表现不佳，且渲染质量与效率有限。为此，我们将人脸视频帧提升至统一的动态3D高斯场，确保帧间结构与时间连贯性。同时，设计了一种新型神经高斯纹理机制，不仅支持复杂风格编辑，还实现了超过100FPS的渲染速度。该方法通过大规模2D生成模型的知识蒸馏融合多模态输入，并引入表情相似性引导与面部感知编辑模块，有效缓解迭代数据更新带来的退化问题。大量实验验证了方法在时序一致性、编辑效率和渲染质量上的优越性。应用涵盖文本驱动编辑、图像驱动编辑与再打光等，展示了其在视频编辑领域的巨大潜力。演示视频与代码已公开于项目页面：https://ustc3dv.github.io/PortraitGen/
原文摘要 · Abstract (English)
We introduce PortraitGen, a powerful portrait video editing method that achieves consistent and expressive stylization with multimodal prompts. Traditional portrait video editing methods often struggle with 3D and temporal consistency, and typically lack in rendering quality and efficiency. To address these issues, we lift the portrait video frames to a unified dynamic 3D Gaussian field, which ensures structural and temporal coherence across frames. Furthermore, we design a novel Neural Gaussian Texture mechanism that not only enables sophisticated style editing but also achieves rendering speed over 100FPS. Our approach incorporates multimodal inputs through knowledge distilled from large-scale 2D generative models. Our system also incorporates expression similarity guidance and a face-aware portrait editing module, effectively mitigating degradation issues associated with iterative dataset updates. Extensive experiments demonstrate the temporal consistency, editing efficiency, and superior rendering quality of our method. The broad applicability of the proposed approach is demonstrated through various applications, including text-driven editing, image-driven editing, and relighting, highlighting its great potential to advance the field of video editing. Demo videos and released code are provided in our project page: https://ustc3dv.github.io/PortraitGen/
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