🎉 We are excited to announce that our IDOL paper has been accepted to CVPR 2025! Join our IDOL community and explore its applications!

## Demo Video

Watch the demo video for a first look at IDOL in action. Experience the impressive capabilities of our method as it brings single images to life, reconstructing photorealistic, animatable 3D humans with remarkable accuracy and detail.

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## Abstract

This work introduces IDOL, a feed-forward, single-image human reconstruction framework that is fast, high-fidelity, and generalizable. Leveraging a large-scale dataset of 100K multi-view subjects, our method demonstrates exceptional generalizability and robustness in handling diverse human shapes, cross-domain data, severe viewpoints, and occlusions. With a uniform structured representation, the reconstructed avatars are directly animatable and easily editable, providing a significant step forward for various applications in graphics, vision, and beyond.

Our method (a) provides a fast and high-fidelity feed-forward single-image human reconstruction pipeline; (b) leverages a large-scale multi-view human dataset to handle diverse shapes, domains, viewpoints, and occlusions; (c) produces structured, animatable, and easily editable avatars.

## Introduction

Explore how IDOL redefines single-image 3D human reconstruction. This introductory video presents the method's core ideas, providing a clear overview of how our framework achieves photorealistic reconstruction, seamless animation/editing, and robust performance across challenging scenarios.

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## Pipeline

The architecture of IDOL, a fully differentiable transformer-based framework for reconstructing animatable 3D human from a single image. The model integrates a high-resolution encoder and fuses image tokens with learnable UV tokens through the UV-Alignment Transformer. A UV Decoder predicts Gaussian attribute maps as intermediate representations, capturing the human's geometry and appearance in a structured 2D UV space defined by the SMPL-X model. These maps, in conjunction with the SMPL-X model, represent a 3D human avatar in a canonical space, which can be animated using linear blend skinning (LBS). The model is optimized using multi-view images with diverse poses and identities, learning to disentangle pose, appearance, and shape.

## Dataset

HuGe100K

We introduce a large-scale multi-view human dataset containing over 100K photorealistic subjects for noncommercial research purposes. The dataset supports robust 3D reconstruction tasks and facilitates research advancements in human modeling.

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### Apply for Dataset Access

To access the dataset, please submit an application using the link below: [Dataset Access]

## Animation Results

Given a reference image that provides the target character's identity and a reference video that supplies the pose, our method animates the reconstructed human to match the movements observed in the video.
Using the uniform 3D representation and SMPL-X pose parameters, IDOL achieves precise control over the avatar's pose, including detailed finger movements.

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## Citation

If you find our work useful, please cite it using the following format:

                @misc{zhuang2024idolinstantphotorealistic3d,
                    title={IDOL: Instant Photorealistic 3D Human Creation from a Single Image},
                    author={Yiyu Zhuang and Jiaxi Lv and Hao Wen and Qing Shuai and Ailing Zeng and Hao Zhu and Shifeng Chen and Yujiu Yang and Xun Cao and Wei Liu},
                    year={2024},
                    eprint={2412.14963},
                    archivePrefix={arXiv},
                    primaryClass={cs.CV},
                    url={https://arxiv.org/abs/2412.14963},
              }

Welcome all the visitors to IDOL!
Feel free to contact us with any questions or feedback.

Contact Us:

- Yiyu Zhuang (yiyu.zhuang@smail.nju.edu.cn)
- Ailing Zeng (ailingzengzzz@gmail.com)
- Hao Zhu (zhuhaoese@nju.edu.cn)