Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks

Official implementation code for Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks (EG 2023).

Abstract: There has been significant progress in generating an animatable 3D human avatar from a single image. However, recovering texture for the 3D human avatar from a single image has been relatively less addressed. Because the generated 3D human avatar reveals the occluded texture of the given image as it moves, it is critical to synthesize the occluded texture pattern that is unseen from the source image. To generate a plausible texture map for 3D human avatars, the occluded texture pattern needs to be synthesized with respect to the visible texture from the given image. Moreover, the generated texture should align with the surface of the target 3D mesh. In this paper, we propose a texture synthesis method for a 3D human avatar that incorporates geometry information. The proposed method consists of two convolutional networks for the sampling and refining process. The sampler network fills in the occluded regions of the source image and aligns the texture with the surface of the target 3D mesh using the geometry information. The sampled texture is further refined and adjusted by the refiner network. To maintain the clear details in the given image, both sampled and refined texture is blended to produce the final texture map. To effectively guide the sampler network to achieve its goal, we designed a curriculum learning scheme that starts from a simple sampling task and gradually progresses to the task where the alignment needs to be considered. We conducted experiments to show that our method outperforms previous methods qualitatively and quantitatively.

Environments

We recommend to use Docker. Use seokg1023/vml-pytorch:vessl for the docker image.

docker pull seokg1023/vml-pytorch:vessl

All dependencies for the environment are provided in requirements.txt.

pip install -r requirements.txt

Todos

• data preparation (data structure, preprocessing, dataloader)
• network checkpoint
• evaluation code

Setup

Docker

modify the code below or simply use run_docker.sh to start the docker container

docker run --gpus all \
    -it --rm --shm-size=8gb \
    seokg1023/vml-pytorch:vessl

Download checkpoints

The pretrained network checkpoints can be downloaded from here. Download and unzip the checkpoint files and place it as below:

SampleRefine
   └── checkpoints
         ├── R1-mirror-vis_mask_c
         └── S1-mirror-norm_map_vis_mask

Data preparation (tdb)

The data needs to be pre-processed using DensePose and Tex2Shape.
The required pre-processing steps are needed:

1. Obtain indexed UV (IUV) using DensePose from source image
2. Obtain partial texture maps from both source and mirrored view using UV lookup table
3. Produce a symmetric texture map by merging both partial texture maps.

You can also refer to Pose-with-Style for producing symmetric texture map.
To obtain the normal map, Please refer to github repository of Tex2Shape.

(Data preprocessing code will be added soon) \

The created dataset should be prepared as below:

Dataset
   ├── symmetry (partial texture)
   │     ├── -90
   │     ├── ...
   │     └── 90
   │          ├── image_0000_symmetry.png
   │          ├── ...
   │          └── image_0100_symmetry.png
   ├── mask_symm
   │     ├── -90
   │     ├── ...
   │     └── 90
   ├── normal_map
   │     ├── -90
   │     ├── ...
   │     └── 90
   └── texture_map
         ├── image_0000_texture_map.png
         ├── ...
         └── image_0100_texture_map.png

Demo

use run_SamplerNet.sh to run SamplerNet only
use run_RefinerNet.sh to run full pipeline

bash run_SamplerNet.sh --gpu 0 # choose gpu device
bash run_RefinerNet.sh --gpu 0 # choose gpu device

Training

use train-SamplerNet.sh to train the SamplerNet
use train-RefinerNet.sh to train the RefinerNet (requires trained SamplerNet)

bash train-SamplerNet.sh --gpu 0 # choose gpu device
bash train-RefinerNet.sh --gpu 0 # choose gpu device

Evaluation

TBD

Citation

@inproceedings{cha2023generating,
  title={Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks},
  author={Cha, Sihun and Seo, Kwanggyoon and Ashtari, Amirsaman and Noh, Junyong},
  booktitle={Computer Graphics Forum},
  volume={42},
  number={2},
  pages={385--396},
  year={2023},
  organization={Wiley Online Library}
}

@incollection{cha2022generating,
  title={Generating 3D Human Texture from a Single Image with Sampling and Refinement},
  author={Cha, Sihun and Seo, Kwanggyoon and Ashtari, Amirsaman and Noh, Junyong},
  booktitle={ACM SIGGRAPH 2022 Posters},
  pages={1--2},
  year={2022}
}