IDOL: Instant Photorealistic 3D Human Creation from a Single Image
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.
67690222fbd79d33cf57d51d
Yiyu Zhuang
claimed_verified
2024-12-23T11:09:48.780Z
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yiyuzhuang
user
yiyuzhuang
67690222fbd79d33cf57d51e
Jiaxi Lv
67690222fbd79d33cf57d51f
Hao Wen
67690222fbd79d33cf57d520
Qing Shuai
67690222fbd79d33cf57d521
Ailing Zeng
67690222fbd79d33cf57d522
Hao Zhu
67690222fbd79d33cf57d523
Shifeng Chen
67690222fbd79d33cf57d524
Yujiu Yang
67690222fbd79d33cf57d525
Xun Cao
67690222fbd79d33cf57d526
Wei Liu
IDOL: Instant Photorealistic 3D Human Creation from a Single Image
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.
A scalable transformer model using a large dataset of high-quality human images achieves fast and high-fidelity 3D avatar reconstruction from a single image with support for animation and editing.
HUman-centric GEnerated dataset HuGe100K pose-controllable image-to-multi-view model feed-forward transformer 3D human Gaussian representation human pose body shape clothing geometry texture editing