DAD-3DHeads

Why we included this project

DAD-3DHeads is the official codebase behind the CVPR 2022 paper of the same name, built for anyone doing single-image 3D head alignment, dense head reconstruction, or head-pose estimation. The dataset pairs head images with FLAME-topology meshes whose dense annotations support training, and the repo's evaluation code implements the paper's protocol, measuring reprojection error, relative depth ordering, Chamfer distance, and pose accuracy, so you can score your own predictions on the validation and test splits locally, with ground-truth labels now published for both. You also get training scripts and a couple of newer utilities, including a UV texture predictor and a PNCC estimator. For teams comparing face-fitting methods, that adds up to a reproducible baseline for judging pose fitting and face or head shape matching rather than just another static dataset release. One thing to check before you adopt it: the CC BY-NC-SA license restricts use to non-commercial research, so read it carefully if you have a commercial product in mind.