title: TripoSR: Fast 3D Object Reconstruction from a Single Image
summary: This technical report introduces TripoSR, a 3D reconstruction model
  leveraging transformer architecture for fast feed-forward 3D generation,
  producing 3D mesh from a single image in under 0.5 seconds. Building upon the
  LRM network architecture, TripoSR integrates substantial improvements in data
  processing, model design, and training techniques. Evaluations on public
  datasets show that TripoSR exhibits superior performance, both quantitatively
  and qualitatively, compared to other open-source alternatives. Released under
  the MIT license, TripoSR is intended to empower researchers, developers, and
  creatives with the latest advancements in 3D generative AI.
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paper_authors.name: Christian Laforte
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paper_authors.fullname: Christian Laforte
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paper_authors.name: Varun Jampani
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paper_authors.fullname: Varun Jampani
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paper_authors.name: Yan-Pei Cao
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paper_title: TripoSR: Fast 3D Object Reconstruction from a Single Image
paper_summary: This technical report introduces TripoSR, a 3D reconstruction model
  leveraging transformer architecture for fast feed-forward 3D generation,
  producing 3D mesh from a single image in under 0.5 seconds. Building upon the
  LRM network architecture, TripoSR integrates substantial improvements in data
  processing, model design, and training techniques. Evaluations on public
  datasets show that TripoSR exhibits superior performance, both quantitatively
  and qualitatively, compared to other open-source alternatives. Released under
  the MIT license, TripoSR is intended to empower researchers, developers, and
  creatives with the latest advancements in 3D generative AI.
paper_ai_keywords: transformer architecture 3D reconstruction model fast feed-forward 3D generation 3D mesh LRM network architecture