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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T172800
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UID:siggraphasia_SIGGRAPH Asia 2024_sess137_papers_920@linklings.com
SUMMARY:MV2MV: Multi-View Image Translation via View-Consistent Diffusion 
 Models
DESCRIPTION:Technical Papers\n\nYoucheng Cai, Runshi Li, and Ligang Liu (U
 niversity of Science and Technology of China)\n\nImage translation has var
 ious applications in computer graphics and computer vision, aiming to tran
 sfer images from one domain to another. Thanks to the excellent generation
  capability of diffusion models, recent single-view image translation meth
 ods achieve realistic results. However, directly applying diffusion models
  for multi-view image translation remains challenging for two major obstac
 les: the need for paired training data and the limited view consistency. T
 o overcome the obstacles, we present a unified multi-view image to multi-v
 iew image translation framework based on diffusion models, called MV2MV. F
 irstly, we propose a novel self-supervised training strategy that exploits
  the success of off-the-shelf single-view image translators and the 3D Gau
 ssian Splatting (3DGS) technique to generate pseudo ground truths as super
 visory signals, leading to enhanced consistency and fine details. Addition
 ally, we propose a latent multi-view consistency block, which utilizes the
  latent-3DGS as the underlying 3D representation to facilitate information
  exchange across multi-view images and inject 3D prior into the diffusion 
 model to enforce consistency. Finally, our approach simultaneously optimiz
 es the diffusion model and 3DGS to achieve a better trade-off between cons
 istency and realism. Extensive experiments across various translation task
 s demonstrate that MV2MV outperforms task-specific specialists in both qua
 ntitative and qualitative.\n\nRegistration Category: Full Access, Full Acc
 ess Supporter\n\nLanguage Format: English Language\n\nSession Chair: Micha
 el Rubinstein (Google)
URL:https://asia.siggraph.org/2024/program/?id=papers_920&sess=sess137
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