title: Efficient 3D Articulated Human Generation with Layered Surface Volumes
summary: Access to high-quality and diverse 3D articulated digital human assets is
  crucial in various applications, ranging from virtual reality to social
  platforms. Generative approaches, such as 3D generative adversarial networks
  (GANs), are rapidly replacing laborious manual content creation tools. However,
  existing 3D GAN frameworks typically rely on scene representations that
  leverage either template meshes, which are fast but offer limited quality, or
  volumes, which offer high capacity but are slow to render, thereby limiting the
  3D fidelity in GAN settings. In this work, we introduce layered surface volumes
  (LSVs) as a new 3D object representation for articulated digital humans. LSVs
  represent a human body using multiple textured mesh layers around a
  conventional template. These layers are rendered using alpha compositing with
  fast differentiable rasterization, and they can be interpreted as a volumetric
  representation that allocates its capacity to a manifold of finite thickness
  around the template. Unlike conventional single-layer templates that struggle
  with representing fine off-surface details like hair or accessories, our
  surface volumes naturally capture such details. LSVs can be articulated, and
  they exhibit exceptional efficiency in GAN settings, where a 2D generator
  learns to synthesize the RGBA textures for the individual layers. Trained on
  unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality
  and view-consistent 3D articulated digital humans without the need for
  view-inconsistent 2D upsampling networks.
paper_authors._id: 64ae22447ac35b7de07d7bbf
paper_authors.name: Yinghao Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-01T09:28:32.425Z
paper_authors._id: 6555da9adcf410fd0753569c
paper_authors.avatarUrl: /avatars/ac58a796bb54f334fdb475a4b75c4d27.svg
paper_authors.fullname: Yinghao Xu
paper_authors.type: user
paper_authors.user: justimyhxu
paper_authors._id: 64ae22447ac35b7de07d7bc0
paper_authors.name: Wang Yifan
paper_authors._id: 64ae22447ac35b7de07d7bc1
paper_authors.name: Alexander W. Bergman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-13T08:32:18.944Z
paper_authors._id: 64aa5898221e6292c92c8683
paper_authors.avatarUrl: /avatars/47c783125c64811856d31e1ca8f7755a.svg
paper_authors.fullname: Alexander Bergman
paper_authors.type: user
paper_authors.user: ab77
paper_authors._id: 64ae22447ac35b7de07d7bc2
paper_authors.name: Menglei Chai
paper_authors._id: 64ae22447ac35b7de07d7bc3
paper_authors.name: Bolei Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-13T08:32:22.914Z
paper_authors._id: 64aedf11c693a19547999a59
paper_authors.avatarUrl: /avatars/83a54b7c217168c91cea15dd000f4af2.svg
paper_authors.fullname: Bolei Zhou
paper_authors.type: user
paper_authors.user: zhoubolei
paper_authors._id: 64ae22447ac35b7de07d7bc4
paper_authors.name: Gordon Wetzstein
paper_title: Efficient 3D Articulated Human Generation with Layered Surface Volumes
paper_summary: Access to high-quality and diverse 3D articulated digital human assets is
  crucial in various applications, ranging from virtual reality to social
  platforms. Generative approaches, such as 3D generative adversarial networks
  (GANs), are rapidly replacing laborious manual content creation tools. However,
  existing 3D GAN frameworks typically rely on scene representations that
  leverage either template meshes, which are fast but offer limited quality, or
  volumes, which offer high capacity but are slow to render, thereby limiting the
  3D fidelity in GAN settings. In this work, we introduce layered surface volumes
  (LSVs) as a new 3D object representation for articulated digital humans. LSVs
  represent a human body using multiple textured mesh layers around a
  conventional template. These layers are rendered using alpha compositing with
  fast differentiable rasterization, and they can be interpreted as a volumetric
  representation that allocates its capacity to a manifold of finite thickness
  around the template. Unlike conventional single-layer templates that struggle
  with representing fine off-surface details like hair or accessories, our
  surface volumes naturally capture such details. LSVs can be articulated, and
  they exhibit exceptional efficiency in GAN settings, where a 2D generator
  learns to synthesize the RGBA textures for the individual layers. Trained on
  unstructured, single-view 2D image datasets, our LSV-GAN generates high-quality
  and view-consistent 3D articulated digital humans without the need for
  view-inconsistent 2D upsampling networks.
paper_ai_keywords: generative adversarial networks (GANs) template meshes volumes fast differentiable rasterization Layered Surface Volumes (LSVs) RGBA textures GAN settings view-consistent 3D articulated digital humans 2D image datasets 2D upsampling networks