Awesome
HuManiFlow
Code repository for the paper:
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation
Akash Sengupta, Ignas Budvytis, Roberto Cipolla
CVPR 2023
[paper+supplementary][video]
This paper presents a probabilistic approach to 3D human shape and pose estimation, which aims to improve sample-input consistency and sample diversity over contemporary methods.
This repository contains inference, training and evaluation code. A few weaknesses of this approach, and future research directions, are listed below. If you find this code useful in your research, please cite the following publication:
@InProceedings{sengupta2023humaniflow,
author = {Sengupta, Akash and Budvytis, Ignas and Cipolla, Roberto},
title = {{HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation}},
booktitle = {CVPR},
month = {June},
year = {2023}
}
Installation
Requirements
- Linux or macOS
- Python ≥ 3.7
Instructions
First clone the repo:
git clone https://github.com/akashsengupta1997/HuManiFlow.git
We recommend using a virtual environment to install relevant dependencies:
python3.8 -m venv HuManiFlow_env
source HuManiFlow_env/bin/activate
Install torch and torchvision (the code has been tested with v1.9.0 of torch), as well as other dependencies:
pip install torch==1.9.0 torchvision==0.10.0
pip install -r requirements.txt
Finally, install pytorch3d, which we use for data generation during training and visualisation during inference. To do so, you will need to first install the CUB library following the instructions here. Then you may install pytorch3d - note that the code has been tested with v0.7.1 of pytorch3d, and we recommend installing this version using:
pip install "git+https://github.com/facebookresearch/pytorch3d.git@v0.7.1"
Model files
You will need to download SMPL model files from here. The neutral model is required for training and running the demo code. If you want to evaluate the model on datasets with gendered SMPL labels (such as 3DPW and SSP-3D), you should also download the male and female models. You may need to convert the SMPL model files to be compatible with Python 3 by removing any chumpy objects. To do so, please follow the instructions here.
Download pre-trained model checkpoints for our 3D Shape/Pose network, as well as for 2D Pose HRNet-W48 from here.
Place the SMPL model files and network checkpoints in the model_files directory, which should have the following structure.
If the files are placed elsewhere, you will need to update configs/paths.py accordingly.
HuManiFlow
├── model_files # Folder with model files
│ ├── smpl
│ │ ├── SMPL_NEUTRAL.pkl # Gender-neutral SMPL model
│ │ ├── SMPL_MALE.pkl # Male SMPL model
│ │ ├── SMPL_FEMALE.pkl # Female SMPL model
│ ├── humaniflow_weights.tar # HuManiFlow checkpoint
│ ├── pose_hrnet_w48_384x288.pth # Pose2D HRNet checkpoint
│ ├── cocoplus_regressor.npy # Cocoplus joints regressor
│ ├── J_regressor_h36m.npy # Human3.6M joints regressor
│ ├── J_regressor_extra.npy # Extra joints regressor
│ └── UV_Processed.mat # DensePose UV coordinates for SMPL mesh
└── ...
Inference
scripts/run_predict.py is used to run inference on a given folder of input images. For example, to run inference on the demo folder, do:
python scripts/run_predict.py --image_dir assets/demo_images/ --save_dir pred_output/ -VS -VU -VXYZ
This will first detect human bounding boxes in the input images using Mask-RCNN. If your input images are already cropped and centred around the subject of interest,
you may skip this step using --cropped_images as an option. To greatly increase inference speed, remove the options -VS -VXYZ to skip directional variance and
sample visualisation, and only render the predicted pose and shape point estimate.
scripts/run_optimise.py is used to run post-inference optimisation, with:
python scripts/run_optimise.py --pred_image_dir assets/demo_images/ --pred_output_dir pred_output/ --save_dir opt_output/
This minimises reprojection error between 2D keypoints and the 3D point estimate. The predicted pose and shape distribution is used as an image-conditioned prior, to guide the optimisation process.
Evaluation
scripts/run_evaluate.py is used to evaluate our method on the 3DPW and SSP-3D datasets.
A description of the metrics used to evaluate predicted distributions is given in metrics/eval_metrics_tracker.py, as well as in the paper.
Download SSP-3D from here. Update configs/paths.py with the path pointing to the un-zipped SSP-3D directory.
Evaluate on SSP-3D with:
python scripts/run_evaluate.py -D ssp3d -B 32 -N 100
To change the number of samples used for sample-based distribution evaluation metrics, update the -N argument.
Using more samples will give better measures of sample-input consistency and sample diversity, but will slow down evaluation.
Download 3DPW from here. You will need to preprocess the dataset first, to extract centred+cropped images and SMPL labels (adapted from SPIN):
python data/pw3d_preprocess.py --dataset_path $3DPW_DIR_PATH
This should create a subdirectory with preprocessed files, such that the 3DPW directory has the following structure:
$3DPW_DIR_PATH
├── test
│ ├── 3dpw_test.npz
│ ├── cropped_frames
├── imageFiles
└── sequenceFiles
Additionally, download HRNet 2D joint detections on 3DPW from here,
and place this in $3DPW_DIR_PATH/test. Update configs/paths.py with the path pointing to $3DPW_DIR_PATH/test. Evaluate on 3DPW with:
python scripts/run_evaluate.py -D 3dpw -B 32 -N 10
Training
scripts/run_train.py is used to train our method using random synthetic training data (rendered on-the-fly during training).
Download .npz files containing SMPL training/validation body poses and textures
from here.
Place these files in a ./train_files directory, or update the appropriate variables in configs/paths.py with
paths pointing to the these files. Note that the SMPL textures are
from SURREAL and MultiGarmentNet.
We use images from LSUN as random backgrounds for our synthetic training data.
Specifically, images from the 10 scene categories are used. Instructions to download and extract these images are
provided here. The data/copy_lsun_images_to_train_files_dir.py script can be used to copy LSUN background
images to the ./train_files directory, which should have the following structure:
train_files
├── lsun_backgrounds
├── train
├── val
├── smpl_train_poses.npz
├── smpl_train_textures.npz
├── smpl_val_poses.npz
└── smpl_val_textures.npz
Finally, start training with:
python scripts/run_train.py -E experiments/exp_001
As a sanity check, the script should find 91106 training poses, 125 + 792 training textures, 397582 training backgrounds, 33347 validation poses, 32 + 76 validation textures and 3000 validation backgrounds.
Weaknesses and Future Research
The following aspects of our method may be the subject of future research:
- Sample diversity: 3D samples do not always cover the range of solutions that match an input image - in particular, samples involving limbs bending backwards are rarely obtained.
- Mesh interpenetrations: this occurs occasionally amongst 3D mesh samples drawn from shape and pose distribution predictions. A sample inter-penetratation penalty may be useful.
- Non-tight clothing: body shape prediction accuracy suffers when subjects are wearing non-tight clothing, since the synthetic training data does not model clothing in 3D (only uses clothing textures). Perhaps better synthetic data (e.g. AGORA) will alleviate this issue.
TODO
- Gendered pre-trained models for improved shape estimation
Acknowledgments
Code was adapted from/influenced by the following repos - thanks to the authors!