README.md

Learnable Triangulation of Human Pose

This repository is an official PyTorch implementation of the paper "Learnable Triangulation of Human Pose" (ICCV 2019, oral). Here we tackle the problem of 3D human pose estimation from multiple cameras. We present 2 novel methods — Algebraic and Volumetric learnable triangulation — that outperform previous state of the art.

If you find a bug, have a question or know to improve the code - please open an issue!

How to use

This project doesn't have any special or difficult-to-install dependencies. All installation can be down with:

pip install -r requirements.txt

Data

Note: only Human3.6M dataset training/evaluation is available right now. CMU Panoptic dataset will be added soon.

Human3.6M

1. Download and preprocess the dataset by following the instructions in mvn/datasets/human36m_preprocessing/README.md.
2. Place the preprocessed dataset to data/human36m. If you don't want to store the dataset in the directory with code, just create a soft symbolic link: ln -s {PATH_TO_HUMAN36M_DATASET} ./data/human36m.
3. Download pretrained backbone's weights from here and place them here: data/pretrained/human36m/pose_resnet_4.5_pixels_human36m.pth (ResNet-152 trained on COCO dataset and finetuned jointly on MPII and Human3.6M).
4. If you want to train Volumetric model, you need rough estimations of the 3D skeleton both for train and val splits. You have two options:

• Rough 3D skeletons can be estimated by Algebraic model and placed to data/precalculated_results/human36m/results_train.pkl and data/precalculated_results/human36m/results_val.pkl respectively.
• Other option is to use the ground truth (GT) estimate of the 3D skeleton by setting use_gt_pelvis: true in a config file. Here you don't need any precalculated results, but such training mode overestimates the resulting accuracy, because pelvis is always perfectly defined.

CMU Panoptic

Will be added soon

Train

Every experiment is defined by .config files. Configs with experiments from the paper can be found in experiments directory (results can be found below):

Human3.6M:

1. Algebraic w/o confidences — experiments/human36m/train/human36m_alg_no_conf.yaml
2. Algebraic w/ confidences — experiments/human36m/train/human36m_alg.yaml
3. Volumetric (softmax aggregation) — experiments/human36m/train/human36m_vol_softmax.yaml
4. Volumetric (softmax aggregation, GT pelvis) — experiments/human36m/train/human36m_vol_softmax_gtpelvis.yaml

CMU Panoptic

Will be added soon

Single-GPU

To train a Volumetric model with softmax aggregation and GT-estimated pelvises using 1 GPU, run:

python3 train.py \
  --config experiments/human36m/train/human36m_vol_softmax_gtpelvis.yaml \
  --logdir ./logs

The training will start with the config file specified by --config, and logs (including tensorboard files) will be stored in --logdir.

Multi-GPU (in testing)

Multi-GPU training is implemented with PyTorch's DistributedDataParallel. It can be used both for single-machine and multi-machine (cluster) training. To run the processes use the PyTorch launch utility.

To train a Volumetric model with softmax aggregation and GT-estimated pelvises using 2 GPUs on single machine, run:

python3 -m torch.distributed.launch --nproc_per_node=2 --master_port=2345 \
  train.py  \
  --config experiments/human36m/train/human36m_vol_softmax_gtpelvis.yaml \
  --logdir ./logs

Tensorboard

To watch your experiments' progress, run tensorboard:

tensorboard --logdir ./logs

Evaluation

After training, you can evaluate the model. Inside the same config file, add path to the learned weights (they are dumped to logs dir during training):

model:
    init_weights: true
    checkpoint: {PATH_TO_WEIGHTS}

Also, you can change other config parameters like retain_every_n_frames_test.

Run:

python3 train.py \
  --eval --eval_dataset val \
  --config experiments/human36m/eval/human36m_vol_softmax.yaml \
  --logdir ./logs

Argument --eval_dataset can be val or train. Results can be seen in logs directory or in the tensorboard.

Results

• We conduct experiments on two available large multi-view datasets: Human3.6M [2] and CMU Panoptic [3].
• The main metric is MPJPE (Mean Per Joint Position Error) which is L2 distance averaged over all joints.

Human3.6M

• We significantly improved upon the previous state of the art (error is measured relative to pelvis, without alignment).
• Our best model reaches 17.7 mm error in absolute coordinates, which was unattainable before.
• Our Volumetric model is able to estimate 3D human pose using any number of cameras, even using only 1 camera. In single-view setup, we get results comparable to current state of the art [6] (49.9 mm vs. 49.6 mm).

MPJPE relative to pelvis:

MPJPE (averaged across all actions), mm Multi-View Martinez [4] 57.0 Pavlakos et al. [8] 56.9 Tome et al. [4] 52.8 Kadkhodamohammadi & Padoy [5] 49.1 Qiu et al. [9] 26.2 RANSAC (our implementation) 27.4 Ours, algebraic 22.6 Ours, volumetric 20.8
MPJPE absolute (scenes with invalid ground-truth annotations are excluded):

MPJPE (averaged across all actions), mm RANSAC (our implementation) 22.8 Ours, algebraic 19.2 Ours, volumetric 17.7
MPJPE relative to pelvis (single-view methods):

MPJPE (averaged across all actions), mm Martinez et al. [7] 62.9 Sun et al. [6] 49.6 Ours, volumetric single view 49.9

CMU Panoptic

• Our best model reaches 13.7 mm error in absolute coordinates for 4 cameras
• We managed to get much smoother and more accurate 3D pose annotations compared to dataset annotations (see video demonstration)

MPJPE relative to pelvis [4 cameras]:

MPJPE, mm RANSAC (our implementation) 39.5 Ours, algebraic 21.3 Ours, volumetric 13.7

Method overview

We present 2 novel methods of learnable triangulation: Algebraic and Volumetric.

Algebraic

Our first method is based on Algebraic triangulation. It is similar to the previous approaches, but differs in 2 critical aspects:

1. It is fully differentiable. To achieve this, we use soft-argmax aggregation and triangulate keypoints via a differentiable SVD.
2. The neural network additionally predicts scalar confidences for each joint, passed to the triangulation module, which successfully deals with outliers and occluded joints.

For the most popular Human3.6M dataset, this method already dramatically reduces error by 2.2 times (!), compared to the previous art.

Volumetric

In Volumetric triangulation model, intermediate 2D feature maps are densely unprojected to the volumetric cube and then processed with a 3D-convolutional neural network. Unprojection operation allows dense aggregation from multiple views and the 3D-convolutional neural network is able to model implicit human pose prior.

Volumetric triangulation additionally improves accuracy, drastically reducing the previous state-of-the-art error by 2.4 times! Even compared to the best parallelly developed method by MSRA group, our method still offers significantly lower error of 21 mm.

Cite us!

@inproceedings{iskakov2019learnable,
  title={Learnable Triangulation of Human Pose},
  author={Iskakov, Karim and Burkov, Egor and Lempitsky, Victor and Malkov, Yury},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year={2019}
}

Contributors

• Karim Iskakov
• Egor Burkov
• Victor Lempitsky
• Yury Malkov
• Rasul Kerimov
• Ivan Bulygin

News

8 Oct 2019: Code is released!

References

• [1] R. Hartley and A. Zisserman. Multiple view geometry in computer vision.
• [2] C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments.
• [3] H. Joo, T. Simon, X. Li, H. Liu, L. Tan, L. Gui, S. Banerjee, T. S. Godisart, B. Nabbe, I. Matthews, T. Kanade,S. Nobuhara, and Y. Sheikh. Panoptic studio: A massively multiview system for social interaction capture.
• [4] D. Tome, M. Toso, L. Agapito, and C. Russell. Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture.
• [5] A. Kadkhodamohammadi and N. Padoy. A generalizable approach for multi-view 3D human pose regression.
• [6] X. Sun, B. Xiao, S. Liang, and Y. Wei. Integral human pose regression.
• [7] J. Martinez, R. Hossain, J. Romero, and J. J. Little. A simple yet effective baseline for 3d human pose estimation.
• [8] G. Pavlakos, X. Zhou, K. G. Derpanis, and K. Daniilidis. Harvesting multiple views for marker-less 3D human pose annotations.
• [9] H. Qiu, C. Wang, J. Wang, N. Wang and W. Zeng. (2019). Cross View Fusion for 3D Human Pose Estimation, GitHub