title: Single-View 3D Human Digitalization with Large Reconstruction Models
summary: In this paper, we introduce Human-LRM, a single-stage feed-forward Large
  Reconstruction Model designed to predict human Neural Radiance Fields (NeRF)
  from a single image. Our approach demonstrates remarkable adaptability in
  training using extensive datasets containing 3D scans and multi-view capture.
  Furthermore, to enhance the model's applicability for in-the-wild scenarios
  especially with occlusions, we propose a novel strategy that distills
  multi-view reconstruction into single-view via a conditional triplane diffusion
  model. This generative extension addresses the inherent variations in human
  body shapes when observed from a single view, and makes it possible to
  reconstruct the full body human from an occluded image. Through extensive
  experiments, we show that Human-LRM surpasses previous methods by a significant
  margin on several benchmarks.
paper_authors._id: 65af54cd1f418c74494151a3
paper_authors.name: Zhenzhen Weng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T11:15:27.845Z
paper_authors._id: 630834fbf0dc38fb47c245b2
paper_authors.avatarUrl: /avatars/579d083fdbfba7331b07941f0bef83a4.svg
paper_authors.fullname: Zhenzhen Weng
paper_authors.type: user
paper_authors.user: zzweng
paper_authors._id: 65af54cd1f418c74494151a4
paper_authors.name: Jingyuan Liu
paper_authors._id: 65af54cd1f418c74494151a5
paper_authors.name: Hao Tan
paper_authors._id: 65af54cd1f418c74494151a6
paper_authors.name: Zhan Xu
paper_authors._id: 65af54cd1f418c74494151a7
paper_authors.name: Yang Zhou
paper_authors._id: 65af54cd1f418c74494151a8
paper_authors.name: Serena Yeung-Levy
paper_authors._id: 65af54cd1f418c74494151a9
paper_authors.name: Jimei Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T11:16:45.570Z
paper_authors._id: 65ae93c53e82498d4cdaa428
paper_authors.avatarUrl: /avatars/b03221ed402247f6e7a80efef834574f.svg
paper_authors.fullname: Yang
paper_authors.type: user
paper_authors.user: Jimei
paper_title: Single-View 3D Human Digitalization with Large Reconstruction Models
paper_summary: In this paper, we introduce Human-LRM, a single-stage feed-forward Large
  Reconstruction Model designed to predict human Neural Radiance Fields (NeRF)
  from a single image. Our approach demonstrates remarkable adaptability in
  training using extensive datasets containing 3D scans and multi-view capture.
  Furthermore, to enhance the model's applicability for in-the-wild scenarios
  especially with occlusions, we propose a novel strategy that distills
  multi-view reconstruction into single-view via a conditional triplane diffusion
  model. This generative extension addresses the inherent variations in human
  body shapes when observed from a single view, and makes it possible to
  reconstruct the full body human from an occluded image. Through extensive
  experiments, we show that Human-LRM surpasses previous methods by a significant
  margin on several benchmarks.
paper_ai_keywords: Human-LRM Large Reconstruction Model Neural Radiance Fields (NeRF) diffusion model conditional triplane diffusion model