@ Joint2SMPL baseline finished. Numpy, Tensorflow and PyTorch implementation of SMPL model. For any questions, feel free to contact me.

3D-pose to SMPL parameters regressor: training complete, merging with smpl_torch_batch Module.

I write a PyTorch implementation based on CalciferZh's Tensorflow code, which supports batch processing and GPU training. The implementation is hosted in smpl_torch.py along with the testing example.

The implementation is tested under Ubuntu 18.04, Python 3.6 and Pytorch 1.0.0 stable. The output is the same as the original Tensorflow implementation, as can be tested with test.py.

I wrote this because the author-provided implementation was mainly based on chumpy in Python 2, which is kind of unpopular. Meanwhile, the official version cannot run on GPU.

This numpy version is faster (since some computations were rewrote in a vectorized manner) and easier to understand (hope so), and the TensorFlow version can run on GPU.

For more details about SMPL model, see SMPL.

1. Download the model file here.

2. Run python preprocess.py /PATH/TO/THE/DOWNLOADED/MODEL to preprocess the official model. preprocess.py will create a new file model.pkl. smpl_np.py and smpl_tf.py both rely on model.pkl. NOTE: the official pickle model contains chumpy object, so prerocess.py requires chumpy to extract official model. You need to modify chumpy's cource code a little bit to make it compatible to preprocess.py (and Python 3). Here is an instruction in Chinese about this.

3. Run python smpl_np.py or python smpl_tf.py or python smpl_torch.py to see the example. Additionally, run python smpl_torch_batch.py for batched support.