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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T163000
DTEND;TZID=Asia/Tokyo:20241205T164100
UID:siggraphasia_SIGGRAPH Asia 2024_sess137_papers_411@linklings.com
SUMMARY:Portrait Video Editing Empowered by Multimodal Generative Priors
DESCRIPTION:Technical Papers\n\nXuan Gao, Haiyao Xiao, Chenglai Zhong, Shi
 min Hu, Yudong Guo, and Juyong Zhang (University of Science and Technology
  of China)\n\nWe 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 3
 D and temporal consistency, and typically lack in rendering quality and ef
 ficiency. To address these issues, we lift the portrait video frames to a 
 unified dynamic 3D Gaussian field, which ensures structural and temporal c
 oherence across frames. Furthermore, we design a novel Neural Gaussian tex
 ture 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-aw
 are portrait editing module, effectively mitigating degradation issues ass
 ociated with iterative dataset updates. Extensive experiments demonstrate 
 the temporal consistency, editing efficiency, and superior rendering quali
 ty of our method. The broad applicability of the proposed approach is demo
 nstrated through various applications, including text-driven editing, imag
 e-driven editing, and relighting, highlighting its great potential to adva
 nce the field of video editing.\n\nRegistration Category: Full Access, Ful
 l Access Supporter\n\nLanguage Format: English Language\n\nSession Chair: 
 Michael Rubinstein (Google)
URL:https://asia.siggraph.org/2024/program/?id=papers_411&sess=sess137
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