Requirements

python3
pytorch
scipy
chumpy
psbody.mesh

Code works with psbody.mesh v0.4 , pytorch >= v1.0 , chumpy v0.7 and scipy v1.3 .

How to Run

• Download and prepare SMPL model and 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)
  • This link has all the weights listed above as well as the following:
    • pant_female_weights
    • pant_male_weights
    • short-pant_female_weights
    • short-pant_male_weights
    • skirt_female_weights
• Set output path in run_SACANet.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_SACANet.py render. (Blender 2.79 needs to be installed.)

Training by yourself

• 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 SACANetNet 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.

1.No module named 'smpl_lib' :export PYTHONPATH=/.../TailorNet_dataset:$PYTHONPATH

2.No blender :export PATH="/home/cyx/cyx/blender-2.79-linux-glibc219-x86_64:$PATH"(your own path)

We have provided three available models for selection.