title: En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D
    Synthetic Data
summary: We present En3D, an enhanced generative scheme for sculpting high-quality 3D
  human avatars. Unlike previous works that rely on scarce 3D datasets or limited
  2D collections with imbalanced viewing angles and imprecise pose priors, our
  approach aims to develop a zero-shot 3D generative scheme capable of producing
  visually realistic, geometrically accurate and content-wise diverse 3D humans
  without relying on pre-existing 3D or 2D assets. To address this challenge, we
  introduce a meticulously crafted workflow that implements accurate physical
  modeling to learn the enhanced 3D generative model from synthetic 2D data.
  During inference, we integrate optimization modules to bridge the gap between
  realistic appearances and coarse 3D shapes. Specifically, En3D comprises three
  modules: a 3D generator that accurately models generalizable 3D humans with
  realistic appearance from synthesized balanced, diverse, and structured human
  images; a geometry sculptor that enhances shape quality using multi-view normal
  constraints for intricate human anatomy; and a texturing module that
  disentangles explicit texture maps with fidelity and editability, leveraging
  semantical UV partitioning and a differentiable rasterizer. Experimental
  results show that our approach significantly outperforms prior works in terms
  of image quality, geometry accuracy and content diversity. We also showcase the
  applicability of our generated avatars for animation and editing, as well as
  the scalability of our approach for content-style free adaptation.
paper_authors._id: 6594d157f0152a21fcb20252
paper_authors.name: Yifang Men
paper_authors._id: 6594d157f0152a21fcb20253
paper_authors.name: Biwen Lei
paper_authors._id: 6594d157f0152a21fcb20254
paper_authors.name: Yuan Yao
paper_authors._id: 6594d157f0152a21fcb20255
paper_authors.name: Miaomiao Cui
paper_authors._id: 6594d157f0152a21fcb20256
paper_authors.name: Zhouhui Lian
paper_authors._id: 6594d157f0152a21fcb20257
paper_authors.name: Xuansong Xie
paper_title: En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D
    Synthetic Data
paper_summary: We present En3D, an enhanced generative scheme for sculpting high-quality 3D
  human avatars. Unlike previous works that rely on scarce 3D datasets or limited
  2D collections with imbalanced viewing angles and imprecise pose priors, our
  approach aims to develop a zero-shot 3D generative scheme capable of producing
  visually realistic, geometrically accurate and content-wise diverse 3D humans
  without relying on pre-existing 3D or 2D assets. To address this challenge, we
  introduce a meticulously crafted workflow that implements accurate physical
  modeling to learn the enhanced 3D generative model from synthetic 2D data.
  During inference, we integrate optimization modules to bridge the gap between
  realistic appearances and coarse 3D shapes. Specifically, En3D comprises three
  modules: a 3D generator that accurately models generalizable 3D humans with
  realistic appearance from synthesized balanced, diverse, and structured human
  images; a geometry sculptor that enhances shape quality using multi-view normal
  constraints for intricate human anatomy; and a texturing module that
  disentangles explicit texture maps with fidelity and editability, leveraging
  semantical UV partitioning and a differentiable rasterizer. Experimental
  results show that our approach significantly outperforms prior works in terms
  of image quality, geometry accuracy and content diversity. We also showcase the
  applicability of our generated avatars for animation and editing, as well as
  the scalability of our approach for content-style free adaptation.
paper_ai_keywords: 3D generative scheme Gaussian Neural Implicit Representations Tensor-Web Compiling Co-Design 3D generator geometry sculptor multi-view normal Constraints texturing module semantical UV partitioning differentiable rasterizer synthetic 2D data generalizable 3D humans realistic appearance balanced, diverse, and structured human images high-quality 3D avatars content diversity image quality geometry accuracy