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Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function

About

In this paper, we propose a novel architecture to recover extremely detailed 3D faces in roughly 10 seconds. Unlike previous learning-based methods that regularize the cost volume via 3D CNN, we propose to learn an implicit function for regressing the matching cost. By fitting a 3D morphable model from multi-view images, the features of multiple images are extracted and aggregated in the mesh-attached UV space, which makes the implicit function more effective in recovering detailed facial shape.

Our Pipeline

Our pipeline consists of three parts: base mesh ftting (green blocks), implicit function learning (blue blocks), and mesoscopic recovery (pink block).

Citation

@InProceedings{xiao2022detailed,
author = {Xiao, Yunze and Zhu, Hao and Yang, Haotian and Diao, Zhengyu and Lu, Xiangju and Cao, Xun},
title = {Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2022}
}

How to use

Installation

We recommend to use Python 3.6 for the following instruction

• pip install -r requirements.txt

Install psbody-mesh from here.

Download

Models

Before running the scripts, please download the predefined data from here. And unpack them to folder ./predfine_data/
Please download the meso model from here. And put it into folder ./dpmap_pred/checkpoints/

Dataset

• For demo, you can download a small set of data from here
• For the FaceScape Dataset used in our paper, there are two alternative ways to obtain.
  1. You can download the original images and 3D models(raw scan) from FaceScape Dataset. And unpack the data to the FACESCAPE_DATA_FOLDER Some metadata from link to DATA_FOLDER.
  2. You can download our processed images which only contains 10 views with lower resolution. These images are generated by running python preprocess_images.py. You should also get the ground truth 3D models from FaceScape Dataset too. Considering all the 3D models are too large, you can use our base mesh and related maps which are generated by running python gen_maps.py. The base mesh and related maps that we offer may have some differences from those generated by the script because of the code version. But it has low influence on the final results. Download our processed data by link.
     And unpack the data to the DATA_FOLDER.

Then we recommend to run the following scripts.

• Building the structure of the data folder python mkdirs.py (You need set dataroot=DATA_FOLDER in mkdirs.py)
• Processing images from FaceScape Dataset python preprocess_images.py (You can skip it if you use the data we offer). Before running, you should set the dataroot related args(e.g. facescape_dataroot as FACESCAPE_DATA_FOLDER)

Dataset metadata information

• select_dict.json -- index of used images.

{ 
  person_id: {
    expression_id: index_list
  }
}

• id.json -- index of trainset and evalset
• Rt_scale_dict.json -- scale from model to millimeter(mm)

{
  person_id: {
    expression_id: [scale, Rt]
  }
}

• facial_mask.png -- mask in uv

Evaling

Our method consists of several parts in the following order.

• Run python gen_maps.py for base mesh fitting
• Run python eval_if.py for implicit function learning
  lower num_sample in eval_if.py for lower GPU memory occupation
• Run python eval_reg.py for post regularization
• Run python gen_tex.py for blended texture
• Run cd dpmap_pred && python main.py --input DATAFOLDER/pred/texture_relocated --output DATAFOLDER/pred/dp_map for mesoscopic prediction

If you want to use dpmap to get mesh of mesoscopic prediction, set the folder in dpmap_pred/scripts/dpmap2mesh.py and run python dpmap_pred/scripts/dpmap2mesh.py. It may cost some time to generate results.

You can modifiy the config file options.py or use args --ARGS in command like python eval_if.py --d_size 201
Before running, you should set the dataroot related args(fit_dataroot, if_dataroot, reg_dataroot) as DATA_FOLDER

Traning

We recommend to use a GPU with high memory(>= 24GB) to train our model(implicit function learning stage).

Our method consists of several parts in the following order.

• Run python gen_maps.py and edit mod="train" in gen_maps.py for generating base mesh
• Run python train_if.py for training implicit function learning module
• Run python eval_if.py and edit mod="train" in eval_if.py for generating trainset of post regularization
• Run python train_reg.py for training post regularization