Portrait Video Editing Empowered by Multimodal Generative Priors
Authors: name: Xuan Gao, name: Haiyao Xiao, name: Chenglai Zhong, name: Shimin Hu, name: Yudong Guo, name: Juyong Zhang

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/