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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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TZOFFSETFROM:+0900
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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T163000
DTEND;TZID=Asia/Tokyo:20241205T174000
UID:siggraphasia_SIGGRAPH Asia 2024_sess137@linklings.com
SUMMARY:Diffuse and Conquer
DESCRIPTION:Technical Papers\n\nEach Paper gives a 10 minute presentation.
 \n\nPortrait Video Editing Empowered by Multimodal Generative Priors\n\nWe
  introduce PortraitGen, a powerful portrait video editing method that achi
 eves consistent and expressive stylization with multimodal prompts. Tradit
 ional portrait video editing methods often struggle with 3D and temporal c
 onsistency, and typically lack in rendering quality and efficiency. To add
 re...\n\n\nXuan Gao, Haiyao Xiao, Chenglai Zhong, Shimin Hu, Yudong Guo, a
 nd Juyong Zhang (University of Science and Technology of China)\n---------
 ------------\nMV2MV: Multi-View Image Translation via View-Consistent Diff
 usion Models\n\nImage translation has various applications in computer gra
 phics and computer vision, aiming to transfer images from one domain to an
 other. Thanks to the excellent generation capability of diffusion models, 
 recent single-view image translation methods achieve realistic results. Ho
 wever, directly appl...\n\n\nYoucheng Cai, Runshi Li, and Ligang Liu (Univ
 ersity of Science and Technology of China)\n---------------------\nStyleCr
 after: Taming Stylized Video Diffusion with Reference-Augmented Adapter Le
 arning\n\nText-to-video (T2V) models have shown remarkable capabilities in
  generating diverse videos. However, they struggle to produce user-desired
  artistic videos due to (i) text's inherent clumsiness in expressing speci
 fic styles and (ii) the generally degraded style fidelity. To address thes
 e challenges, ...\n\n\nGongye Liu (Tsinghua University); Menghan Xia, Yong
  Zhang, and Haoxin Chen (Tencent AI lab); Jinbo Xing (Chinese University o
 f Hong Kong); Yibo Wang (Tsinghua University); Xintao Wang and Ying Shan (
 Tencent); and Yujiu Yang (Tsinghua University)\n---------------------\nSta
 bleNormal: Reducing Diffusion Variance for Stable and Sharp Normal\n\nThis
  work addresses the challenge of high-quality surface normal estimation fr
 om monocular colored inputs (i.e., images and videos), a field which has r
 ecently been revolutionized by repurposing diffusion priors. However, prev
 ious attempts still struggle with stochastic inference, conflicting with t
 ...\n\n\nChongjie Ye and Lingteng Qiu (FNii, The Chinese University of Hon
 g Kong, Shenzhen; SSE, The Chinese University of Hong Kong, Shenzhen); Xia
 odong Gu and Qi Zuo (Alibaba); Yushuang Wu (FNii, The Chinese University o
 f Hong Kong, Shenzhen; SSE, The Chinese University of Hong Kong, Shenzhen)
 ; Zilong Dong and Liefeng Bo (Alibaba); Yuliang Xiu (Max Planck Institute 
 for Intelligent Systems); and Xiaoguang Han (SSE, The Chinese University o
 f Hong Kong, Shenzhen; FNii, The Chinese University of Hong Kong, Shenzhen
 )\n---------------------\nHyperGAN-CLIP: A Unified Framework for Domain Ad
 aptation, Image Synthesis and Manipulation\n\nGenerative Adversarial Netwo
 rks (GANs), particularly StyleGAN and its variants, have demonstrated rema
 rkable capabilities in generating highly realistic images. Despite their s
 uccess, adapting these models to diverse tasks such as domain adaptation, 
 reference-guided synthesis, and text-guided manipu...\n\n\nAbdul Basit Ane
 es (Koç University), Ahmet Canberk Baykal (University of Cambridge), Muham
 med Burak Kizil (Koç University), Duygu Ceylan (Adobe Research), Erkut Erd
 em (Hacettepe University), and Aykut Erdem (Koç University)\n-------------
 --------\nFast High-Resolution Image Synthesis with Latent Adversarial Dif
 fusion Distillation\n\nDiffusion models are the main driver of progress in
  image and video synthesis, but suffer from slow inference speed. Distilla
 tion methods, like the recently introduced adversarial diffusion distillat
 ion (ADD) aim to shift the model from many-shot to single-step inference, 
 albeit at the cost of expen...\n\n\nAxel Sauer, Frederic Boesel, Tim Dockh
 orn, Andreas Blattmann, Patrick Esser, and Robin Rombach (Black Forest Lab
 s)\n\nRegistration Category: Full Access, Full Access Supporter\n\nLanguag
 e Format: English Language\n\nSession Chair: Michael Rubinstein (Google)
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