• 📣 [2022-10-19] We have populated the ETH main building with Microsoft Hololens 2. The code and apps are released here! Read its license before use.

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]

• 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.

• 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

• 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/*

• 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.

• 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.

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

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

• 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.

• 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.

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

• 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.

• 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.

• 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.

• 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

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.