This repository contains training and inference code for the following paper:

TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style  
Chaitanya Patel*, Zhouyingcheng Liao*, Gerard Pons-Moll  
CVPR 2020 (ORAL)  

[ArXiv] [Project Website] [Dataset Repo] [Youtube]

old-t-shirt_female	t-shirt_female	t-shirt_male

• [02-08-2020] Female and male pant weights added.
• [19-07-2020] Male shirt weights added.
• [12-07-2020] Female shirt weights added.
• [28-06-2020] Female t-shirt weights added.
• [25-06-2020] Minor bug fixes and male t-shirt weights added.
• [17-06-2020] Inference script and female old-t-shirt weights added.

python3
pytorch
scipy
chumpy
psbody.mesh

• Download and prepare SMPL model and TailorNet data from dataset repository.
• Set DATA_DIR and SMPL paths in global_var.py file accordingly.
• Download trained model weights in a directory and set its path to MODEL_WEIGHTS_PATH variable in global_var.py.
  • old-t-shirt_female_weights (4.1 GB)
  • t-shirt_male_weights (2.0 GB)
  • t-shirt_female_weights (2.0 GB)
  • shirt_female_weights (2.5 GB)
  • shirt_male_weights (2.5 GB)
  • pant_female_weights and pant_male_weights available here
  • [Other garments coming soon]
• Set output path in run_tailornet.py and run it to predict garments on some random inputs. You can play with different inputs. You can also run inference on motion sequence data.
• To visualize predicted garment using blender, run python run_tailornet.py render. (Blender needs to be installed.)

... evaluated using evaluate function in utils/eval.py.

• Set global variables in global_var.py, especially LOG_DIR where training logs will be stored.
• Set config variables like gender and garment class in trainer/base_trainer.py (or pass them via command line) and run python trainer/base_trainer.py to train TailorNet MLP baseline.
• Similarly, run python trainer/lf_trainer.py to train low frequency predictor and trainer/ss2g_trainer.py to train shape-style-to-garment(in canonical pose) model.
• Run python trainer/hf_trainer.py --shape_style <shape1>_<style1> <shape2>_<style2> ... to train pivot high frequency predictors for pivots <shape1>_<style1>, <shape2>_<style2>, and so on. See DATA_DIR/<garment_class>_<gender>/pivots.txt to know available pivots.
• Use models.tailornet_model.TailorNetModel with appropriate logdir arguments to do prediction.

Cite us if you use our model, code or data:

@inproceedings{patel20tailornet,
        title = {TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style},
        author = {Patel, Chaitanya and Liao, Zhouyingcheng and Pons-Moll, Gerard},
        booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
        month = {jun},
        organization = {{IEEE}},
        year = {2020},
    }

• Thanks to Bharat for many fruitful discussions and for smpl_lib library taken from his MultiGarmentNet repo's lib folder.
• Thanks to Garvita for helping out during the onerous procedure of data generation.

For any doubt or concert about the code, raise an issue on this repository.