PyTorch implementation of CloudWalk's recent paper DenseBody.
Note: For most recent updates, please check out the dev branch.
Here is the reproduction result (left: input image; middle: ground truth UV position map; right: estimated UV position map)
- SMPL official UV map is now supported! Please checkout
- Code reformating complete! Please refer to
data_utils/UV_map_generator.pyfor more details.
- Thanks Raj Advani for providing new hand crafted UV maps!
Please follow the instructions
PREPS.md to prepare your training dataset and UV maps. Then run
nohup_train.sh to begin training.
To train with your own UV map, checkout
UV_MAPS.md for detailed instructions.
To explore different network architectures, checkout
NETWORKS.md for detailed instructions.
[x] Creating ground truth UV position maps for Human36m dataset.
- [x] 20190329 Finish UV data processing.
- [x] 20190331 Align SMPL mesh with input image.
- [x] 20190404 Data washing: Image resize to 256*256 and 2D annotation compensation.
- [x] 20190411 Generate and save UV position map.
- [x] radvani Hand parsed new 3D UV data
- [x] Validity checked with minor artifacts (see results below)
- [x] Making UV_map generation module a separate class.
- [x] 20190413 Prepare ground truth UV maps for washed dataset.
- [x] 20190417 SMPL official UV map supported!
- [x] 20190613 A testing toy dataset has been released!
[x] Prepare baseline model training
Lingbo Yang(Lotayou): The owner and maintainer of this repo.
Raj Advani(radvani): Provide several hand-crafted UV maps and many constructive feedbacks.