SMPL

Numpy, TensorFlow and PyTorch implementation of SMPL model.

Update Feb 2nd 2019

Now we have a faster PyTorch implementation, and we also support SMIL model. For more details, please check this PR.

Overview

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 project provides Numpy, TensorFlow and PyTorch implementation of SMPL model.

For more details about SMPL model, see SMPL.

Numpy and Tensorflow Implementation

Contributor: CalciferZh.

The 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.

PyTorch Implementation with Batch Input

Contributor: Lotayou.

The PyTorch version is derived from the Tensorflow version, and in addition 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.

Usage

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. If you don't want to install chumpy, you can download processed file from BaiduYunDisk with extraction code vblg

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.