Official Pytorch implementation for 2020 3DV paper "PLACE: Proximity Learning of Articulation and Contact in 3D Environments"
[Project] [Paper] [2-min Video] [7-min Video]
The code has been tested on Ubuntu 18.04, python 3.6 and CUDA 10.0. Please install the following packages/models following their instructions:
- install SMPL-X, and download SMPL-X models
- install Chamfer Pytorch (source code included in our repo,
chamfer_pytorchfolder) - download the VPoser model (source code included in our repo,
human_body_priorfolder)
Then run pip install -r requirements.txt to install other dependencies.
It is noticed that different versions of smplx and VPoser will influece generation results.
PROX dataset can be downloaded here. The following contents are required for this project:
body_segmentsthe body segments of PROXcam2worldcamera to world transform matrixPROXDbody mesh of PROXsdfscene SDFs of PROXscenesthe full scene mesh of PROX (optional)scenes_downsampleddownsampled scene mesh can be downloaded here
Note that you can use either scenes or scenes_downsampled for preprocessing / training / testing. In the paper, we use scenes_downsampled for the whole pipeline.
We use 7 scenes from Matterport3d for evaluation. Please download scene meshes here and download the computed scene SDFs here Please organize the matterport3D dataset folder as following:
mp3d
├── sdf
| ├── SCENE_NAME_sdf.npy
| ├── SCENE_NAME.json
├── SCENE_NAME.ply
- 7 scenes used for evaluation:
17DRP5sb8fy-bedroom,17DRP5sb8fy-familyroomlounge,17DRP5sb8fy-livingroom,sKLMLpTHeUy-familyname_0_1,X7HyMhZNoso-livingroom_0_16,zsNo4HB9uLZ-bedroom0_0,zsNo4HB9uLZ-livingroom0_13 sdfscene SDFs for room meshesSCENE_NAME.plyroom meshes
Download Replica dataset here, and organize the dataset folder as following:
replica_v1
├── sdf
| ├── SCENE_NAME_sdf.npy
| ├── SCENE_NAME.json
├── SCENE_NAME
| ├── mesh.ply
- 5 scenes used for evaluation:
office_2,hotel_0,room_0,frl_apartment_0,apartment_1
The trained checkpoints can be downloaded here.
Unzip the downloaded checkpoints folder, and put it in the project folder.
Project
├── checkpoints
| ├── body_dec_last_model.pkl
| ├── cVAE_last_model.pkl
| ├── sceneBpsAE_last_model.pkl
| ├── sceneBpsVertsAE_last_model.pkl
|── ...
We provide quick demo demo.ipynb to generate random bodies given a scene mesh.
Please install all required dependencies and download the checkpoints first.
Compute the bps encoding and prepare training data with PROX dataset for each scene separately:
python compute_bps_encoding.py --dataset_path PATH/TO/PROX --scene_name SCENE/NAME --split train/test --auge_per_sample 4 --preprocess_file_path PATH/TO/PREPROCESS/RESULTS --smplx_model_path PATH/TO/SMPLX/MODEL
- training scenes:
BasementSittingBooth,MPH8,MPH11,MPH112,N0Sofa,N3Library,Werkraum,N3Office, set--split trainand--auge_per_sample 4 - testing scenes:
MPH1Library,MPH16,N0SittingBooth,N3OpenArea, set--split testand--auge_per_sample 1 - preprocessed data for testing scenes are meant for evaluation of training loss during training
Note each training scene is augmented 4 times (--auge_per_sample 4)
and this preprocessing has a heary requirement for CPU memory.
If you run the preprocess script for each training scene multiple times (e.x., 2 times, then set --auge_per_sample 2 each time), the preprocessed files can look like:
Project
├── preprocessed_encoding
| ├── BasementSittingBooth_1_rot_list.npy
| ├── BasementSittingBooth_1_scene_bps_list.npy
| ├── ...
| ├── BasementSittingBooth_2_rot_list.npy
| ├── BasementSittingBooth_2_scene_bps_list.npy
| ├── ...
| ├── ...
| ├── MPH1Library_rot_list.npy
| ├── MPH1Library_scene_bps_list.npy
| ├── ...
| ├── ...
|── ...
With preprocessed data, train the model (remember to modify train_scene_list in train.py if you run preprocess script each scene multiple times):
python train.py --dataset_path PATH/TO/PROX --preprocess_file_path PATH/TO/PREPROCESS/RESULTS --weight_loss_kl 0.5 --weight_loss_contact 0.01 --start_contact_loss 200
Load trained checkpoints, random generate bodies given a scene mesh and implement the interaction-based optimization:
python test_optimize.py --optimize --dataset prox/mp3d/replica --dataset_path PATH/TO/DATASET --scene_name SCENE/NAME --prox_dataset_path PATH/TO/PROX --smplx_model_path PATH/TO/SMPLX/MODEL --vposer_model_path PATH/TO/VPOSE/MODEL --save_path PATH/TO/SAVE/RESULTS
Evaluate the physical plausibility metrics of optimized results for each scene, set --visualize True to visualize results:
python eval_contact_collision.py --dataset prox/mp3d/replica --dataset_path PATH/TO/DATASET --scene_name SCENE/NAME --optimize_result_dir PATH/TO/OPTIMIZE/RESULTS --smplx_model_path PATH/TO/SMPLX/MODEL --vposer_model_path PATH/TO/VPOSE/MODEL
Evaluate the diversity metrics for optimized results for all scenes of each dataset:
python eval_diversity.py --dataset prox/mp3d/replica --optimize_result_dir PATH/TO/OPTIMIZE/RESULTS --smplx_model_path PATH/TO/SMPLX/MODEL
When using the code/figures/data/video/etc., please cite our work
@inproceedings{PLACE:3DV:2020,
title = {{PLACE}: Proximity Learning of Articulation and Contact in {3D} Environments},
author = {Zhang, Siwei and Zhang, Yan and Ma, Qianli and Black, Michael J. and Tang, Siyu},
booktitle = {International Conference on 3D Vision (3DV)},
month = nov,
year = {2020},
month_numeric = {11}
}
Acknowledgments. We sincerely thank Mohamed Hassan for his scene SDF calculation protocol. This work was partially supported by the German Research Foundation (DFG): SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, TP XX, project number: 276693517. Qianli Ma acknowledges the support from the Max Planck ETH Center for Learning Systems.
Disclosure. MJB has received research gift funds from Intel, Nvidia, Adobe, Facebook, and Amazon. While MJB is a part-time employee of Amazon, his research was performed solely at MPI. He is also an investor in Meshcapde GmbH.
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