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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
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UID:siggraphasia_SIGGRAPH Asia 2024_sess137_papers_887@linklings.com
SUMMARY:StableNormal: Reducing Diffusion Variance for Stable and Sharp Nor
 mal
DESCRIPTION:Technical Papers\n\nChongjie Ye and Lingteng Qiu (FNii, The Ch
 inese University of Hong Kong, Shenzhen; SSE, The Chinese University of Ho
 ng Kong, Shenzhen); Xiaodong Gu and Qi Zuo (Alibaba); Yushuang Wu (FNii, T
 he Chinese University of 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, T
 he Chinese University of Hong Kong, Shenzhen; FNii, The Chinese University
  of Hong Kong, Shenzhen)\n\nThis work addresses the challenge of high-qual
 ity surface normal estimation from monocular colored inputs (i.e., images 
 and videos), a field which has recently been revolutionized by repurposing
  diffusion priors. However, previous attempts still struggle with stochast
 ic inference, conflicting with the deterministic nature of the Image2Norma
 l task, and costly ensembling step, which slows down the estimation proces
 s. Our method, StableNormal, mitigates the stochasticity of the diffusion 
 process by reducing inference variance, thus producing “Stable-and-Sharp” 
 normal estimates without any additional ensembling process. StableNormal w
 orks robustly under chal lenging imaging conditions, such as extreme light
 ing, blurring, and low quality. It is also robust against transparent and 
 reflective surfaces, as well as cluttered scenes with numerous objects. Sp
 ecifically, StableNormal employs a coarse-to-fine strategy, which starts w
 ith a one-step normal estimator (YOSO) to derive an initial normal guess, 
 that is relatively coarse but reliable, then followed by a semantic-guided
  refinement process (SG-DRN) that refines the normals to recover geometric
  details. The effectiveness of StableNormal is demonstrated through compet
 itive performance in standard datasets such as DIODE-indoor, iBims, Scanne
 tV2, and NYUv2, and also in various downstream tasks, such as surface reco
 nstruction and normal enhancement. These results evidence that StableNorma
 l retains both the “stability” and “sharpness” for accurate normal estimat
 ion. StableNormal represents a baby attempt to repurpose diffusion priors 
 for deterministic estimation. To democratize this, code and models have be
 en publicly available in hf.co/Stable-X.\n\nRegistration Category: Full Ac
 cess, Full Access Supporter\n\nLanguage Format: English Language\n\nSessio
 n Chair: Michael Rubinstein (Google)
URL:https://asia.siggraph.org/2024/program/?id=papers_887&sess=sess137
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