IDOL: Instant Photorealistic 3D Human Creation from a Single Image
Authors: name: Yiyu Zhuang, name: Jiaxi Lv, name: Hao Wen, name: Qing Shuai, name: Ailing Zeng, name: Hao Zhu, name: Shifeng Chen, name: Yujiu Yang, name: Xun Cao, name: Wei Liu

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.