Awesome

SMPL

Numpy, TensorFlow and PyTorch implementation of SMPL model. For C++ implementation (with PyTorch), please see this repo.

Notes:

If you want to estimate SMPL parameters from a set of sparse keypoint coordinates, please check this repo.
The model file cannot be provided due to copyright reasons.
This project cannot fit a SMPL model to a scan - you can check this paper.

Update Feb 2 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 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 and sebftw

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.

SMIL_torch_batch.py can be very fast, but limited by the memory. It also works with sparse tensors. (Saving a lot of said memory)

Usage

Download the model file here.
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.
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.