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DTSTAMP:20260114T163712Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T164000
DTEND;TZID=Australia/Melbourne:20231213T173100
UID:siggraphasia_SIGGRAPH Asia 2023_sess153@linklings.com
SUMMARY:Anything Can be Neural
DESCRIPTION:VET: Visual Error Tomography for Point Cloud Completion and Hi
 gh-Quality Neural Rendering\n\nIn the last few years, deep neural networks
  opened the doors for big advances in novel view synthesis. Many of these 
 approaches are based on a (coarse) proxy geometry obtained by structure fr
 om motion algorithms. Small deficiencies in this proxy can be fixed by neu
 ral rendering, but larger holes or ...\n\n\nLinus Franke, Darius Rückert, 
 and Laura Fink (Friedrich-Alexander Universität Erlangen-Nürnberg); Matthi
 as Innmann (NavVis GmbH); and Marc Stamminger (Friedrich-Alexander Univers
 ität Erlangen-Nürnberg)\n---------------------\nLayerDiffusion: Layered Co
 ntrolled Image Editing with Diffusion Models\n\nWe propose LayerDiffusion,
  a semantic-based controlled image editing method. Our method enables non-
 rigid editing and attribute modification of specific subjects while preser
 ving unique characteristics and integrating them into new backgrounds.\n\n
 \nPengzhi Li, Qinxuan Huang, Yikang Ding, and Zhiheng Li (Tsinghua Univers
 ity)\n---------------------\nAerial Diffusion: Text Guided Ground-to-Aeria
 l View Synthesis from a Single Image using Diffusion Models\n\nAerial Diff
 usion is one of the first approaches towards unsupervised ground-to-aerial
  view synthesis using a single image and the corresponding text descriptio
 n.\n\n\nDivya Kothandaraman, Tianyi Zhou, Ming Lin, and Dinesh Manocha (Un
 iversity of Maryland College Park)\n---------------------\nLiveNVS: Neural
  View Synthesis on Live RGB-D Streams\n\nExisting real-time RGB-D reconstr
 uction approaches, like Kinect Fusion, lack real-time photo-realistic visu
 alization. This is due to noisy, oversmoothed or incomplete geometry and b
 lurry textures which are fused from imperfect depth maps and camera poses.
  Recent neural rendering methods can overcome...\n\n\nLaura Fink (Friedric
 h-Alexander-Universität Erlangen-Nürnberg, Fraunhofer IIS); Darius Rückert
  and Linus Franke (Friedrich-Alexander-Universität Erlangen-Nürnberg); Joa
 chim Keinert (Fraunhofer IIS); and Marc Stamminger (Friedrich-Alexander-Un
 iversität Erlangen-Nürnberg)\n---------------------\nA Neural Implicit Rep
 resentation for the Image Stack: Depth, All in Focus, and High Dynamic Ran
 ge\n\nIn everyday photography, physical limitations of camera sensors and 
 lenses frequently lead to a variety of degradations in captured images suc
 h as saturation or defocus blur. A common approach to overcome these limit
 ations is to resort to image stack fusion, which involves capturing multip
 le images ...\n\n\nChao Wang (Max-Planck-Institut für Informatik); Ana Ser
 rano (Universidad de Zaragoza); and Xingang Pan, Bin Chen, Hans-Peter Seid
 el, Karol Myszkowski, Christian Theobalt, Krzysztof Wolski, and Thomas Lei
 mkühler (Max-Planck-Institut für Informatik)\n\nRegistration Category: Ful
 l Access\n\nSession Chair: Jonah Brucker-Cohen (Lehman College / CUNY, New
  Inc.)
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