title: IDOL: Instant Photorealistic 3D Human Creation from a Single Image
summary: Creating a high-fidelity, animatable 3D full-body avatar from a single image
  is a challenging task due to the diverse appearance and poses of humans and the
  limited availability of high-quality training data. To achieve fast and
  high-quality human reconstruction, this work rethinks the task from the
  perspectives of dataset, model, and representation. First, we introduce a
  large-scale HUman-centric GEnerated dataset, HuGe100K, consisting of 100K
  diverse, photorealistic sets of human images. Each set contains 24-view frames
  in specific human poses, generated using a pose-controllable
  image-to-multi-view model. Next, leveraging the diversity in views, poses, and
  appearances within HuGe100K, we develop a scalable feed-forward transformer
  model to predict a 3D human Gaussian representation in a uniform space from a
  given human image. This model is trained to disentangle human pose, body shape,
  clothing geometry, and texture. The estimated Gaussians can be animated without
  post-processing. We conduct comprehensive experiments to validate the
  effectiveness of the proposed dataset and method. Our model demonstrates the
  ability to efficiently reconstruct photorealistic humans at 1K resolution from
  a single input image using a single GPU instantly. Additionally, it seamlessly
  supports various applications, as well as shape and texture editing tasks.
paper_authors._id: 67690222fbd79d33cf57d51d
paper_authors.name: Yiyu Zhuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-23T11:09:48.780Z
paper_authors._id: 65f83ad63545cc30502642ff
paper_authors.avatarUrl: /avatars/07838b6b8a5e5f04a623c61548418252.svg
paper_authors.fullname: yiyuzhuang
paper_authors.type: user
paper_authors.user: yiyuzhuang
paper_authors._id: 67690222fbd79d33cf57d51e
paper_authors.name: Jiaxi Lv
paper_authors._id: 67690222fbd79d33cf57d51f
paper_authors.name: Hao Wen
paper_authors._id: 67690222fbd79d33cf57d520
paper_authors.name: Qing Shuai
paper_authors._id: 67690222fbd79d33cf57d521
paper_authors.name: Ailing Zeng
paper_authors._id: 67690222fbd79d33cf57d522
paper_authors.name: Hao Zhu
paper_authors._id: 67690222fbd79d33cf57d523
paper_authors.name: Shifeng Chen
paper_authors._id: 67690222fbd79d33cf57d524
paper_authors.name: Yujiu Yang
paper_authors._id: 67690222fbd79d33cf57d525
paper_authors.name: Xun Cao
paper_authors._id: 67690222fbd79d33cf57d526
paper_authors.name: Wei Liu
paper_title: IDOL: Instant Photorealistic 3D Human Creation from a Single Image
paper_summary: Creating a high-fidelity, animatable 3D full-body avatar from a single image
  is a challenging task due to the diverse appearance and poses of humans and the
  limited availability of high-quality training data. To achieve fast and
  high-quality human reconstruction, this work rethinks the task from the
  perspectives of dataset, model, and representation. First, we introduce a
  large-scale HUman-centric GEnerated dataset, HuGe100K, consisting of 100K
  diverse, photorealistic sets of human images. Each set contains 24-view frames
  in specific human poses, generated using a pose-controllable
  image-to-multi-view model. Next, leveraging the diversity in views, poses, and
  appearances within HuGe100K, we develop a scalable feed-forward transformer
  model to predict a 3D human Gaussian representation in a uniform space from a
  given human image. This model is trained to disentangle human pose, body shape,
  clothing geometry, and texture. The estimated Gaussians can be animated without
  post-processing. We conduct comprehensive experiments to validate the
  effectiveness of the proposed dataset and method. Our model demonstrates the
  ability to efficiently reconstruct photorealistic humans at 1K resolution from
  a single input image using a single GPU instantly. Additionally, it seamlessly
  supports various applications, as well as shape and texture editing tasks.
paper_ai_keywords: HUman-centric GEnerated dataset (HuGe100K) feed-forward transformer model 3D Gaussian representation image-to-multi-view model disentanglement human pose body shape clothing geometry texture