多模態生成先驗強化的人像影片編輯

Portrait Video Editing Empowered by Multimodal Generative Priors

September 20, 2024

作者: Xuan Gao, Haiyao Xiao, Chenglai Zhong, Shimin Hu, Yudong Guo, Juyong Zhang

cs.AI

摘要

我們介紹了PortraitGen，一種強大的肖像視頻編輯方法，通過多模態提示實現了一致且具有表現力的風格化。傳統的肖像視頻編輯方法通常在3D和時間一致性方面遇到困難，並且通常在渲染質量和效率方面缺乏。為了應對這些問題，我們將肖像視頻幀提升到統一的動態3D高斯場，確保幀間的結構和時間一致性。此外，我們設計了一種新穎的神經高斯紋理機制，不僅實現了複雜的風格編輯，還實現了超過100FPS的渲染速度。我們的方法通過從大規模2D生成模型中提煉的知識，將多模態輸入納入其中。我們的系統還包括表情相似性指導和面部感知的肖像編輯模塊，有效地緩解了與迭代數據集更新相關的降級問題。大量實驗證明了我們方法的時間一致性、編輯效率和優越的渲染質量。所提出方法的廣泛應用性通過各種應用得到展示，包括文本驅動編輯、圖像驅動編輯和重新照明，突出了其推動視頻編輯領域發展的巨大潛力。在我們的項目頁面提供了演示視頻和發布的代碼：https://ustc3dv.github.io/PortraitGen/

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/