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DTSTAMP:20250522T212946Z
LOCATION:Mile High 1
DTSTART;TZID=America/Denver:20240730T143000
DTEND;TZID=America/Denver:20240730T144000
UID:siggraph_SIGGRAPH 2024_sess115_papers_916@linklings.com
SUMMARY:CharacterGen: Efficient 3D Character Generation From Single Images
  With Multi-view Pose Canonicalization
DESCRIPTION:Hao-Yang Peng, Jia-Peng Zhang, and Meng-Hao Guo (Tsinghua Univ
 ersity); Yan-Pei Cao (VAST); and Shi-Min Hu (Tsinghua University)\n\nChara
 cterGen introduces a streamlined 3D character generation pipeline, which c
 an generate high-quality canonicalized characters from given images within
  one minute. Additionally, we have curated a dataset, Anime3D, which rende
 rs anime characters in multiple poses and views to train and evaluate our 
 model.\n\nInterest Area: Research & Education\n\nRecording: Livestreamed, 
 Recorded\n\nKeyword: Animation, Geometry, Machine Learning, Modeling\n\nRe
 gistration Category: Full Conference, Full Conference Supporter, Virtual A
 ccess, Exhibitor Full Conference, Tuesday\n\nSession Chair: Stephen Lombar
 di (Google)\n\n
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