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TZID:Asia/Tokyo
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DTSTAMP:20260817T171532Z
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:Xuan Gao, Haiyao Xiao, Chenglai Zhong, Shimin Hu, Yudong Guo, 
 and Juyong Zhang (University of Science and Technology of China)\n\nWe int
 roduce PortraitGen, a powerful portrait video editing method that achieves
  consistent and expressive stylization with multimodal prompts. Traditiona
 l portrait video editing methods often struggle with 3D and temporal consi
 stency, and typically lack in rendering quality and efficiency. To address
  these issues, we lift the portrait video frames to a unified dynamic 3D G
 aussian field, which ensures structural and temporal coherence across fram
 es. Furthermore, we design a novel Neural Gaussian texture mechanism that 
 not only enables sophisticated style editing but also achieves rendering s
 peed over 100FPS. Our approach incorporates multimodal inputs through know
 ledge distilled from large-scale 2D generative models. Our system also inc
 orporates expression similarity guidance and a face-aware portrait editing
  module, effectively mitigating degradation issues associated with iterati
 ve dataset updates. Extensive experiments demonstrate the temporal consist
 ency, editing efficiency, and superior rendering quality of our method. Th
 e broad applicability of the proposed approach is demonstrated through var
 ious applications, including text-driven editing, image-driven editing, an
 d relighting, highlighting its great potential to advance the field of vid
 eo editing.\n\nRegistration Category: Full Access, Full Access Supporter\n
 \nLanguage Format: English Language\n\nSession Chair: Michael Rubinstein (
 Google)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_411&sess=sess137
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