Awesome

GAMMA: The Wanderings of Odysseus in 3D Scenes

This repo contains the official implementation of our paper. If you find our work useful in your research, please consider citing it:

@inproceedings{zhang2022wanderings,
  title={The Wanderings of Odysseus in 3D Scenes},
  author={Zhang, Yan and Tang, Siyu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20481--20491},
  year={2022}
}

[website] [paper] [talk]

License

• Third-party software employs their respective license. Here are some examples:

  • Code/model/data relevant to the SMPL-X body model follows its own license.
  • Code/model/data relevant to the AMASS dataset follows its own license.
  • Blender and its SMPL-X add-on employ their respective license.

• The rests employ the Apache 2.0 license. When using the code, please cite our work as above.

Installation

Environment

• Tested OS: Ubuntu 20.04, Windows 10, Windows 11
• Packages: Primary software packages are listed below. See requirements.txt for details.
  • Python >= 3.8
  • PyTorch >= 1.8
  • Open3D == 0.13.0
  • torchgeometry == 0.1.2
  • smplx
  • vposer: checkpoint version 1.0

Datasets

• AMASS: We downloaded and used the SMPL+H G bodies. To get better performance (e.g. bodies without self-interpenetration), please use SMPL-X G bodies instead. Note that the data formats of SMPL-X G might be different.
• Cubes: Our synthetic scene with cubes as blockers.
  • It includes the scene mesh, the navigation mesh, walking paths.
  • The blender file conrtains the python script to visualize motions.
  • See this use case for more details.
  • When they are downloaded, put them into exp_GAMMAPrimitive/data/Cubes/*

Paths

• To run the code properly it is important to set the paths of data, body model, and others. See exp_GAMMAPrimitive/utils/config_env.py for more details.

AMASS Canonicalization

• required for training and visualizing motion primitives.
• exp_GAMMAPrimitive/utils/utils_canonicalize_amass.py extracts short sub-sequences from AMASS and performs canonicalization.
• run python exp_GAMMAPrimitive/utils/utils_canonicalize_amass.py [num_motion_primitives].
• num_motion_primitives is the number of primitives in each sub-sequence. We set to 1 and 10, respectively. So we prepare two processed datasets.

Models

• CMU 41 marker placement
• SSM2 67 marker placement
• Pre-trained Checkpoints: put these folders into results/exp_GAMMAPrimitive/.

Visualizating Motion in Blender

• Blender 2.93 or above.
• Install the SMPL-X add-on

Motion Generation

Generating and Visualizing Long-term Motions

• Select a pre-trained model policy, e.g. MPVAEPolicy_v0.
• Run the following scripts, and save the results to e.g. results/tmp123/GAMMAVAEComboPolicy_PPO/MPVAEPolicy_v0:

## a faster version with less control in tree search
python exp_GAMMAPrimitive/gen_motion_long_in_Cubes_parallel.py --cfg MPVAEPolicy_v0

## or, a slower version with more control in the tree search
python exp_GAMMAPrimitive/gen_motion_long_in_Cubes.py --cfg MPVAEPolicy_v0

• Visualization in Blender
  • Open Blender, and open exp_GAMMAPrimitive/data/Cubes/synthetic_scene_000.blend.
  • (optional) import the navigation mesh scene_cubes_000_navimesh.obj.
  • Change the path in the python script, and click RUN.

Generating and Visualizing Motion Primitives

• To generate motion primitives, it needs canonicalized AMASS sequences as motion seeds.
• Run e.g.

python exp_GAMMAPrimitive/gen_GAMMAprimitive.py --cfg MPVAECombo_1frame_female_v10

• Set the path in exp_GAMMAPrimitive/vis_GAMMAprimitive.py, and run

python exp_GAMMAPrimitive/vis_GAMMAprimitive.py

• The results will save into the same folder.

Train

Given a model configuration cfg, the checkpoints will be automatically saved into results/exp_GAMMAPrimitive/cfg/*.

Train Body Regressors

• Two kinds of body regressors for female and male, respectively. Here we only show one example.
• Set the model configurations like in exp_GAMMAPrimitive/cfg/MoshRegressor_v3_female.yml.
• To train the model, run

python exp_GAMMAPrimitive/train_GammaRegressor.py --cfg MoshRegressor_v3_female

• Since the SMPL-X model is used for the inverse kinematics loss, training the body regressor is much slower than training the marker predictors.

Train Marker Predictors

• first train marker predictors from scratch with individual motion primitives, and then fine-tune it with longer sub-sequences.
• Set the model configurations like in exp_GAMMAPrimitive/cfg/MPVAE_*frame_*.yml.
• To train the model scratch, run

python exp_GAMMAPrimitive/train_GammaPredictor.py --cfg MPVAE_*frame_v2

• To fine-tune the model with rollout, run

python exp_GAMMAPrimitive/train_GammaPredictor.py --cfg MPVAE_*frame_v4 --resume_training 1

• If the checkpoint is not found, copy the pre-trained checkpoint and rename it to epoch-000.ckp.

Train their Combo (optional)

• We did not observe benefits to train them jointly. But we still leave the code for users to explore. Depending on the motion seed and the gender, there should be 4 combos, here we just show one of them as example.
• Set the model configurations like in exp_GAMMAPrimitive/cfg/MPVAECombo_1frame_female_v10.yml.
• Run python exp_GAMMAPrimitive/train_GammaCombo.py --cfg MPVAECombo_1frame_female_v10.

Train Motion Policy

• The motion policy is trained in a separate setting.
• Set the model configurations like in exp_GAMMAPrimitive/cfg/MPVAEPolicy_v0.yml.
• To train the policy, run

python exp_GAMMAPrimitive/train_GammaPolicy.py --cfg MPVAEPolicy_v0

Acknowledgement

We appreciate Gramazio Kohler Research to provide architecture CAD models for experiments. Visualizations of body motions are largely based on the SMPL-X blender add-on. We appreciate Jonathan Lehner to develop visualizations in Nvidia Omniverse.