title: HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
    Structure Priors
summary: Despite recent advancements in high-fidelity human reconstruction techniques,
  the requirements for densely captured images or time-consuming per-instance
  optimization significantly hinder their applications in broader scenarios. To
  tackle these issues, we present HumanSplat which predicts the 3D Gaussian
  Splatting properties of any human from a single input image in a generalizable
  manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and
  a latent reconstruction transformer with human structure priors that adeptly
  integrate geometric priors and semantic features within a unified framework. A
  hierarchical loss that incorporates human semantic information is further
  designed to achieve high-fidelity texture modeling and better constrain the
  estimated multiple views. Comprehensive experiments on standard benchmarks and
  in-the-wild images demonstrate that HumanSplat surpasses existing
  state-of-the-art methods in achieving photorealistic novel-view synthesis.
paper_authors._id: 66729ae8cbf550c42a1401bb
paper_authors.name: Panwang Pan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-19T20:19:18.500Z
paper_authors._id: 654866e8cd0a5621395f8287
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/654866e8cd0a5621395f8287/4Bccwd1ehn-Ee4T1rId5S.jpeg
paper_authors.fullname: Panwang Pan
paper_authors.type: user
paper_authors.user: paulpanwang
paper_authors._id: 66729ae8cbf550c42a1401bc
paper_authors.name: Zhuo Su
paper_authors._id: 66729ae8cbf550c42a1401bd
paper_authors.name: Chenguo Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T21:08:02.942Z
paper_authors._id: 62e18206926f4892a4c782bd
paper_authors.avatarUrl: /avatars/0f89091a5eb72165d2e860d15b339539.svg
paper_authors.fullname: Chenguo Lin
paper_authors.type: user
paper_authors.user: chenguolin
paper_authors._id: 66729ae8cbf550c42a1401be
paper_authors.name: Zhen Fan
paper_authors._id: 66729ae8cbf550c42a1401bf
paper_authors.name: Yongjie Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T21:08:14.272Z
paper_authors._id: 64c62590ed521f27a41f9338
paper_authors.avatarUrl: /avatars/0e849359c0acac44d2513775cb6d3300.svg
paper_authors.fullname: Zhang Yongjie
paper_authors.type: user
paper_authors.user: yongjie-zhang-mail
paper_authors._id: 66729ae8cbf550c42a1401c0
paper_authors.name: Zeming Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T21:08:21.013Z
paper_authors._id: 65c04adbc63d6a8d7f217c4d
paper_authors.avatarUrl: /avatars/3407dd73d1f5cf41c56cee7542858f93.svg
paper_authors.fullname: Zeming Li
paper_authors.type: user
paper_authors.user: ZemingLi
paper_authors._id: 66729ae8cbf550c42a1401c1
paper_authors.name: Tingting Shen
paper_authors._id: 66729ae8cbf550c42a1401c2
paper_authors.name: Yadong Mu
paper_authors._id: 66729ae8cbf550c42a1401c3
paper_authors.name: Yebin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-19T21:07:43.094Z
paper_authors._id: 62e14dbe4db2175cd2735a80
paper_authors.avatarUrl: /avatars/e6385dcedcb97e1b36281b49210321aa.svg
paper_authors.fullname: Yebin Liu
paper_authors.type: user
paper_authors.user: YebinLiu
paper_title: HumanSplat: Generalizable Single-Image Human Gaussian Splatting with
    Structure Priors
paper_summary: Despite recent advancements in high-fidelity human reconstruction techniques,
  the requirements for densely captured images or time-consuming per-instance
  optimization significantly hinder their applications in broader scenarios. To
  tackle these issues, we present HumanSplat which predicts the 3D Gaussian
  Splatting properties of any human from a single input image in a generalizable
  manner. In particular, HumanSplat comprises a 2D multi-view diffusion model and
  a latent reconstruction transformer with human structure priors that adeptly
  integrate geometric priors and semantic features within a unified framework. A
  hierarchical loss that incorporates human semantic information is further
  designed to achieve high-fidelity texture modeling and better constrain the
  estimated multiple views. Comprehensive experiments on standard benchmarks and
  in-the-wild images demonstrate that HumanSplat surpasses existing
  state-of-the-art methods in achieving photorealistic novel-view synthesis.
paper_ai_summary: HumanSplat predicts 3D Gaussian Splatting properties using a 2D multi-view diffusion model and latent reconstruction transformer, achieving photorealistic novel-view synthesis.
paper_ai_keywords: 3D Gaussian Splatting 2D multi-view diffusion model latent reconstruction transformer human structure priors hierarchical loss photorealistic novel-view synthesis