MeshPose: Unifying DensePose and 3D Body Mesh reconstruction

Inference and Evaluation code for the paper MeshPose: Unifying DensePose and 3D Body Mesh reconstruction (CVPR 2024)

Installation

git clone https://github.com/Snapchat/MeshPose.git

conda create -n meshpose python=3.11
conda activate meshpose

pip install -r requirements.txt

Please download the model weights and place them in ./checkpoints via the links in ./checkpoints/checkpoints.md

The code has been tested on Ubuntu and Mac (on both GPU and CPU-only machines).

Run Demo

To run MeshPose on an image with a bounding box:

python3 inference.py

This will plot the front and side view of the predicted vertices on top of the original image.

Run Video Demo

To run MeshPose on a video using a simple person detector/tracker:

python3 video_demo.py --input_video assets/4812014-hd_1920_1080_30fps.mp4 --do_rendering

This will render the meshes of all detected persons on top of the original video. It will also save the predicted vertices for every frame in a json-file.

To use the --do_rendering option, densepose_eval must be installed in the third_party directory (see below).

Run Image Folder Demo

To run MeshPose on an image folder using a simple person detector/tracker:

python3 images_demo.py --input_folder assets/example_images --do_rendering

This will render the meshes of all detected persons on top of each original image. It will also save the predicted vertices for each image in a json-file.

To use the --do_rendering option, densepose_eval must be installed in the third_party directory (see below).

MeshPose Evaluation on the DensePose Benchmark

Data and Benchmark Preparation

Clone densepose_eval in third_party

cd third_party
git clone https://github.com/MeshPose/densepose_eval.git

and follow its installation instructions.

Download the densepose minival dataset and the UV data into ./DensePose_COCO according to the instructions in ./DensePose_COCO/densepose_dataset.md.

MeshPose Inference on Densepose Minival

The following command will run meshpose on each instance in the evaluation dataset and save the results in output/model_predictions.json

python3 inference_coco.py --output_model_predictions output/model_predictions.json

Evaluation of MeshPose Mesh Alignment on the DensePose Benchmark

python3 evaluate_densepose.py --input_model_predictions output/model_predictions.json --output_densepose_score output/densepose_predictions.txt

General Human Mesh Recovery Evaluation on the DensePose Benchmark

To evaluate another Human Mesh Recovery method on DensePose, create a json file (my_mesh_predictions.json) with the following structure:

[
            {'image_id': $image_id_0,  # int
             'id': $instance_id_0,  # int
             'smpl_z': $verts_z_0,  # (6980, )
             'smpl_xy_proj': $verts_xy_proj_0}  # (6980, 2),
             
            {'image_id': $image_id_1,  # int
             'id': $instance_id_1,  # int
             'smpl_z': $verts_z_1,  # (6980, )
             'smpl_xy_proj': $verts_xy_proj_1}  # (6980, 2),
        ...

]

This is a list of dictionaries, each dictionary corresponding to an instance in DensePose_COCO/densepose_coco_2014_minival.json.

image_id: the COCO image_id of the image containing the instance
id: the COCO id of the instance
smpl_z: a list containing the depth of each vertex (Normalized in (-1, 1))
smpl_xy_proj: a list of tuples (x,y) corresponding to the projection of the mesh on the original image

Please make sure that the smpl_xy_proj coordinates are aligned with the original image.

Note: To accelerate this, you can skip instances that don't have a 'dp_masks' field, as they don't contain DensePose annotations and don't contribute to the metrics.

Once my_mesh_predictions.json is ready, the system can be evaluated via:

python3 evaluate_densepose.py --input_model_predictions my_mesh_predictions.json --output_densepose_score output/my_mesh_predictions.txt

Results are saved in output/my_mesh_predictions.txt. We report the GPSM AR and GPSM AP quantities.

Citing

@InProceedings{Le_2024_CVPR,
    author    = {Le, Eric-Tuan and Kakolyris, Antonis and Koutras, Petros and Tam, Himmy and Skordos, Efstratios and Papandreou, George and G\"uler, Riza Alp and Kokkinos, Iasonas},
    title     = {MeshPose: Unifying DensePose and 3D Body Mesh Reconstruction},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {2405-2414}
}

Contact

For questions about this work please contact [email protected] or [email protected]