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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR 2024 Highlight] Official repository for paper "SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction"
| Date | Stars |
|---|---|
| 2026-07-24 | 273 |
| 2026-07-25 | 273 |
| 2026-07-28 | 273 |
| 2026-07-30 | 273 |
| 2026-08-06 | 273 |
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<div align="center">
<h1>SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction</h1>
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<a href="https://river-zhang.github.io/zechuanzhang//" target="_blank">Zechuan Zhang</a> 
<a href="https://z-x-yang.github.io/" target="_blank">Zongxin Yang✉</a> 
<a href="https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN&oi=ao" target="_blank">Yi Yang</a> 
</div>
<div>
ReLER, CCAI, Zhejiang University
</div>
<div>
<sup>✉</sup>Corresponding Author
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<a href="https://river-zhang.github.io/SIFU-projectpage/" target="_blank">CVPR 2024 Highlight</a>
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<div style="width: 80%; text-align: center; margin:auto;">
<img style="width:100%" src="docs/images/teaser.png">
<em>Figure 1. With just a single image, SIFU is capable of reconstructing a high-quality 3D clothed human model, making it well-suited for practical applications such as 3D printing and scene creation. At the heart of SIFU is a novel Side-view Conditioned Implicit Function, which is key to enhancing feature extraction and geometric precision. Furthermore, SIFU introduces a 3D Consistent Texture Refinement process, greatly improving texture quality and facilitating texture editing with the help of text-to-image diffusion models. Notably proficient in dealing with complex poses and loose clothing, SIFU stands out as an ideal solution for real-world applications.</em>
</div>
:open_book: For more visual results, go checkout our <a href="https://river-zhang.github.io/SIFU-projectpage/" target="_blank">project page</a>
This repository will contain the official implementation of _SIFU_.
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# News
- **[2024/6/18]** Due to visa check problem, the author can not come to the conference center in person. We are sorry about this [sad][cry].
- **[2024/4/5]** Our paper has been accepted as **Highlight** (Top 11.9% of accepted papers)!
- **[2024/2/28]** We release the code of **geometry reconstruction**, including test and inference.
- **[2024/2/27]** SIFU has been accepted by **CVPR 2024**! See you in Seattle!
- **[2023/12/13]** We release the paper on [arXiv](https://arxiv.org/abs/2312.06704).
- **[2023/12/10]** We build the [Project Page](https://river-zhang.github.io/SIFU-projectpage/).
# Installation
- Ubuntu 20 / 18
- **CUDA=11.6 or 11.7 or 11.8, GPU Memory > 16GB**
- Python = 3.8
- PyTorch = 1.13.0 (official [Get Started](https://pytorch.org/get-started/locally/))
We thank @[levnikolaevich](https://github.com/levnikolaevich) and @[GuangtaoLyu](https://github.com/GuangtaoLyu) for provide valuable advice on the installation steps.
If you don't have conda or miniconda, please install that first:
```bash
sudo apt-get update && \
sudo apt-get upgrade -y && \
sudo apt-get install unzip libeigen3-dev ffmpeg build-essential nvidia-cuda-toolkit
mkdir -p ~/miniconda3 && \
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh && \
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 && \
rm -rf ~/miniconda3/miniconda.sh && \
~/miniconda3/bin/conda init bash && \
~/miniconda3/bin/conda init zsh
```
```bash
# close and reopen the shell
git clone https://github.com/River-Zhang/SIFU.git
sudo apt-get install libeigen3-dev ffmpeg
cd SIFU
conda env create -f environment.yaml
conda activate sifu
pip install -r requirements.txt
```
Please download the [checkpoint (google drive)](https://drive.google.com/file/d/13rNSmQI_VaMtwlMBSUaxEGybzJEl5KTi/view?usp=sharing) and place them in ./data/ckpt
Please follow [ICON](https://github.com/YuliangXiu/ICON/blob/master/docs/installation.md) to download the extra data, such as HPS and SMPL (using ```fetch_hps.sh``` and ```fetch_data.sh```). There may be missing files about SMPL, and you can download from [here](https://huggingface.co/lilpotat/pytorch3d/tree/main/smpl_data) and put them in /data/smpl_related/smpl_data/.
# Inference
```bash
python -m apps.infer -cExcerpt of 5,925 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5752a205d013b5d2, topic:computer-vision, topic:3d-reconstruction
matched fp:5752a205d013b5d2, topic:pytorch
matched fp:5752a205d013b5d2, topic:diffusion-models, readme:text-to-image