garvita-tiwari/PoseNDF

Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields

This repository contains official implementation of ECCV-2022 paper: Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields (Project Page)

UPDATE:

Please use branch version2

Installation:

Please follow INSTALL.md

Training and Dataset

1. Download AMASS: Store in a folder "amass_raw"". You can train the model for SMPL/SMPL+H or SMPL+X.

https://amass.is.tue.mpg.de/

2.1 Sample poses from AMASS:

python data/sample_poses.py --sampled_pose_dir <path_for_samples_amass_poses> --amass_dir <amass_dataset_dir>

sample_poses.py is based on VPoser data preparation. If you already have this processed data, you can directly use it. You just need to convert .pt file to .npz file.

2.2 Create script for generating training data :

python data/prepare_data.py --raw_data <path_for_samples_amass_poses> --out_path <path_for_training_data> --bash_file ./traindata.sh

If you are using slurm then add "--use_slurm" and change please change the path on environment and machine specs in L24:L30 in data/prepare_data.py

2.3 Create training data :

./traindata.sh

During training the dataloader reads file form data_dir/. You can now delete the amass_raw directory. For all our experiments, we use the same settings as used in VPoser data preparation step.

3. Edit configs/<>.yaml for different experimental setup

experiment:
    root_dir: directory for training data/models and results
model:     #Network acrhitecture
    ......
training:  #Training parameters
    ......
data:       #Training sample details
    .......

Root directory will contain dataset, trained models and results.

4. Training Pose-NDF :

python trainer.py --config=configs/amass.yaml

amass.yaml contains the configs used for the pretrained model.

4. Download pre-trained model : Pretrained model

Inference

Pose-NDF is a continuous model for plausible human poses based on neural distance fields (NDFs). This can be used to project non-manifold points on the learned manifold and hence act as prior for downstream tasks.

Pose generation

python trainer.py --config=configs/amass.yaml --test

This code randomly samples points in input pose space and project them on the learned manifold to generate realsitic poses.

Pose interpolation

 python experiment/interp.py --config=configs/amass.yaml

Motion denoising

 python experiment/motion_denoise.py --config=configs/amass.yaml  --motion_data=<motion data file>

Motion data file is .npz file which contains "body_pose", "betas", "root_orient"

Image based 3d pose estimation

 1. Run openpose to generate 2d keypoints for given image(https://github.com/CMU-Perceptual-Computing-Lab/openpose).
 2. python experiment/image_pose.py --config=configs/amass.yaml  --image_dir=<image data dir>

Both image and corresponding keypoint should be in same directory with <image_name>.jpg and <image_name>.json being the image and 2d keypoints file respectively.

Citation:

@inproceedings{tiwari22posendf,
    title = {Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields},
    author = {Tiwari, Garvita and Antic, Dimitrije and Lenssen, Jan Eric and Sarafianos, Nikolaos and Tung, Tony and Pons-Moll, Gerard},
    booktitle = {European Conference on Computer Vision ({ECCV})},
    month = {October},
    year = {2022},
    }

garvita-tiwari / PoseNDF