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DTSTAMP:20240214T070311Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T114000
DTEND;TZID=Australia/Melbourne:20231214T124000
UID:siggraphasia_SIGGRAPH Asia 2023_sess170@linklings.com
SUMMARY:Magic Diffusion Model
DESCRIPTION:Technical Papers\n\nEnhancing Diffusion Models with 3D Perspec
 tive Geometry Constraints\n\nWhile perspective is a well-studied topic in 
 art, it is generally taken for granted in images. However, for the recent 
 wave of high-quality image synthesis methods such as latent diffusion mode
 ls, perspective accuracy is not an explicit requirement. Since these metho
 ds are capable of outputting a wi...\n\n\nRishi Upadhyay and Howard Zhang 
 (University of California, Los Angeles); Yunhao Ba (University of Californ
 ia, Los Angeles; Sony); Ethan Yang, Blake Gella, and Sicheng Jiang (Univer
 sity of California, Los Angeles); Alex Wong (Yale University); and Achuta 
 Kadambi (University of California, Los Angeles)\n---------------------\nDi
 ffusing Colors: Image Colorization with Text Guided Diffusion\n\nThe color
 ization of grayscale images is a complex and subjective task with signific
 ant challenges. Despite recent progress in employing large-scale datasets 
 with deep neural networks, difficulties with controllability and visual qu
 ality persist. To tackle these issues, we present a novel image color...\n
 \n\nNir Zabari, Aharon Azulay, Alexey Gorkor, and Tavi Halperin (Lightrick
 s) and Ohad Fried (Reichman University)\n---------------------\nExample-Ba
 sed Sampling with Diffusion Models\n\nMuch effort has been put into develo
 ping samplers with specific properties, such as producing blue noise, low-
 discrepancy, lattice or Poisson disk samples. These samplers can be slow i
 f they rely on optimization processes, may rely on a wide range of numeric
 al methods, are not always differentiable....\n\n\nBastien Doignies (Unive
 rsité Claude Bernard Lyon, CNRS); Nicolas Bonneel, David Coeurjolly, and J
 ulie Digne (CNRS, LIRIS); Loïs Paulin (Université Claude Bernard Lyon / CN
 RS, Adobe); and Jean-Claude Iehl and Victor Ostromoukhov (Université Claud
 e Bernard Lyon, CNRS)\n---------------------\nSingle-Image 3D Human Digiti
 zation with Shape-guided Diffusion\n\nWe present an approach to generate a
  360-degree view of a person with a consistent, high-resolution appearance
  from a single input image. NeRF and its variants typically require videos
  or images from different viewpoints. Most existing approaches taking mono
 cular input either rely on ground-truth 3D...\n\n\nBadour AlBahar (Kuwait 
 University); Shunsuke Saito, Hung-Yu Tseng, Changil Kim, and Johannes Kopf
  (Meta); and Jia-Bin Huang (University of Maryland)\n---------------------
 \nLow-Light Image Enhancement with Wavelet-based Diffusion Models\n\nDiffu
 sion models have achieved promising results in image restoration tasks, ye
 t suffer from time-consuming, excessive computational resource consumption
 , and unstable restoration. To address these issues, we propose a robust a
 nd efficient Diffusion-based Low-Light image enhancement approach, dubbed.
 ..\n\n\nHai Jiang (Sichuan University); Ao Luo and Haoqiang Fan (Megvii); 
 Songchen Han (Sichuan University); and Shuaicheng Liu (University of Elect
 ronic Science and Technology of China, Megvii)\n\nRegistration Category: F
 ull Access\n\nSession Chair: Xiangyu Xu (Xi'an Jiaotong University)
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