The code (pytorch for testing & matlab for 3D plot and evaluation) for our project: Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning （2DASL）
MIT License
464
stars
116
forks
source link
How to convert 53K vertices to 43K. (for PRNet evaluation) #34
Hi @XgTu , Thank you for the source codes you released. I'm just wondering how did you compare your method with PRNet or actually how did you evaluate PRNet on AFLW2000 dataset? Coz the PRNet's outputs have 43K vertices while your model's outputs and AFLW2000 ground truth have 53K vertices.
How did you reduce the groundtruth vertices to 43K?
I realized that there is a "exp_base_reduce.mat" file which can produce ground truth with 39K vertices. Where did you get this .mat file and how can reproduce it with 43K vertices?
Thank you in advance for your clarification.
Hi @XgTu , Thank you for the source codes you released. I'm just wondering how did you compare your method with PRNet or actually how did you evaluate PRNet on AFLW2000 dataset? Coz the PRNet's outputs have 43K vertices while your model's outputs and AFLW2000 ground truth have 53K vertices. How did you reduce the groundtruth vertices to 43K? I realized that there is a "exp_base_reduce.mat" file which can produce ground truth with 39K vertices. Where did you get this .mat file and how can reproduce it with 43K vertices? Thank you in advance for your clarification.