​​Densepose Evolution Models & Bootstrapping Pipeline

🔬 The training proceeds in two stages (see image below):
1. First, a master model is trained on data from the source domain (humans with full DensePose annotation S, I, U and V) and supporting domain (animals with segmentation annotation only). Only selected animal classes are chosen from the supporting domain through category filters to guarantee the quality of target domain results. The training is done in a class-agnostic manner: all selected categories are mapped to a single category (human).
2. Second, a student model is trained on data from source and supporting domains, as well as data from target domain obtained by applying the master model, selecting high-confidence detections, and sampling the results.

⚙️ What is included in the GitHub repository:
1. Models that perform estimation of confidence in regressed UV coordinates as well as confidences associated with coarse and fine segmentation.
2. Master and student models trained using the bootstrapping pipeline with chimpanzee as the target category.
3. The source code for the entire pipeline.

🦧 Model Zoo
👨‍🎓 For a more exhaustive explanation of this method please check my older post.