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aims to retarget motion data from the AMASS dataset to various robot models and open-source the retargeted data to facilitate research and applications in robotics and human-robot interaction. AMASS (Archive of Motion Capture as Surface Shapes) is a high-quality human motion capture dataset, and the SMPL-X model is a powerful tool for generating realistic human motion data. + +By adapting the motion data from AMASS to different robot models, we hope to provide a more diverse and accessible motion dataset for robot training and human-robot interaction. + +## Dataset Content + +This open-source project includes the following: +1. **Retargeted Motions**: Motion files retargeted from AMASS to various robot models. + - **bxirobotics elf2**: + + The retargeted motions for the bxirobotics elf2 robot are generated based on the official open-source model: + + https://github.com/bxirobotics/robot_models/tree/main/elf2_dof25 + + The joint positions is not limited, you should limit them during use. + + data shape：[-1,32] + + ​ 0:3 root world position + + ​ 3:7 root quaternion rotation, order: xyzw + + ​ 7:32 joint positions + + joint order: + + ```txt + l_hip_z_joint, + l_hip_x_joint, + l_hip_y_joint, + l_knee_y_joint, + l_ankle_y_joint, + l_ankle_x_joint, + r_hip_z_joint, + r_hip_x_joint, + r_hip_y_joint, + r_knee_y_joint, + r_ankle_y_joint, + r_ankle_x_joint, + waist_z_joint, + waist_x_joint, + waist_y_joint, + l_shld_y_joint, + l_shld_x_joint, + l_shld_z_joint, + l_elb_y_joint, + l_elb_z_joint, + r_shld_y_joint, + r_shld_x_joint, + r_shld_z_joint, + r_elb_y_joint, + r_elb_z_joint + ``` + + + +2. **Usage Examples**: Code examples and tutorials on how to use the retargeted data. + + ./visualize.py + + + +3. **License Files**: Original license information for each sub-dataset within AMASS. + + + +## License + +The retargeted data in this project is derived from the AMASS dataset and therefore adheres to the original license terms of AMASS. Each sub-dataset within AMASS may have different licenses, so please ensure compliance with the following requirements when using the data: +- **Propagate Original Licenses**: When using or distributing the retargeted data, you must include and comply with the original licenses of the sub-datasets within AMASS. +- **Attribution Requirements**: Properly cite this work and the original authors and sources of the AMASS dataset and its sub-datasets. + +For detailed license information, please refer to the `LICENSE` file in this project. + + + +## Acknowledgments + +This project is built on the AMASS dataset and the SMPL-X model. Special thanks to the research team at the Max Planck Institute for Intelligent Systems for providing this valuable resource. + +## Citation + +If you use the data or code from this project, please cite this work and relevant papers for AMASS and SMPL-X: +```bibtex +@misc{Retargeted_AMASS_R, + title={Retargeted AMASS for Robotics}, + author={Kun Zhao}, + url={https://huggingface.co/datasets/fleaven/Retargeted_AMASS_for_robotics} +} + +@inproceedings{AMASS2019, + title={AMASS: Archive of Motion Capture as Surface Shapes}, + author={Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.}, + booktitle={International Conference on Computer Vision (ICCV)}, + year={2019} +} + +@inproceedings{SMPL-X2019, + title={Expressive Body Capture: 3D Hands, Face, and Body from a Single Image}, + author={Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2019} +} +``` + +## Contact + +For any questions or suggestions, please contact: +- **Kun Zhao**: fleaven@hotmail.com diff --git a/amass.bib b/amass.bib new file mode 100644 index 0000000000000000000000000000000000000000..0762435f8ea1568a57c6eb638a867403d42c647f --- /dev/null +++ b/amass.bib @@ -0,0 +1,234 @@ +@conference{AMASS, + title = {{AMASS}: Archive of Motion Capture as Surface Shapes}, + author = {Mahmood, Naureen and Ghorbani, Nima and Troje, Nikolaus F. and Pons-Moll, Gerard and Black, Michael J.}, + booktitle = {International Conference on Computer Vision}, + pages = {5442--5451}, + month = oct, + year = {2019}, + month_numeric = {10} +} + +@misc{AMASS_ACCAD, + title = {{ACCAD MoCap Dataset}}, + author = {{Advanced Computing Center for the Arts and Design}}, + url = {https://accad.osu.edu/research/motion-lab/mocap-system-and-data} +} + +@inproceedings{AMASS_BMLhandball, + author = {Helm, Fabian and Troje, Nikolaus and Reiser, Mathias and Munzert, Jörn}, + year = {2015}, + month = {01}, + pages = {}, + title = {Bewegungsanalyse getäuschter und nicht-getäuschter 7m-Würfe im Handball}, + journal = {47. Jahrestagung der Arbeitsgemeinschaft für Sportpsychologie, Freiburg.} +} + +@article{AMASS_BMLmovi, + title = {{MoVi}: A Large Multipurpose Motion and Video Dataset}, + author = {Saeed Ghorbani and Kimia Mahdaviani and Anne Thaler and Konrad Kording and Douglas James Cook and Gunnar Blohm and Nikolaus F. Troje}, + year = {2020}, + journal = {arXiv preprint arXiv: 2003.01888} +} + +@article{AMASS_BMLrub, + title = {Decomposing Biological Motion: {A} Framework for Analysis and Synthesis of Human Gait Patterns}, + author = {Troje, Nikolaus F.}, + year = 2002, + month = sep, + journal = {Journal of Vision}, + volume = 2, + number = 5, + pages = {2--2}, + doi = {10.1167/2.5.2}, + month_numeric = 9 +} + +@misc{AMASS_CMU, + title = {{CMU MoCap Dataset}}, + author = {{Carnegie Mellon University}}, + url = {http://mocap.cs.cmu.edu} +} + +@article{AMASS_DanceDB, + author = {Aristidou, Andreas and Shamir, Ariel and Chrysanthou, Yiorgos}, + title = {Digital Dance Ethnography: {O}rganizing Large Dance Collections}, + journal = {J. Comput. Cult. Herit.}, + issue_date = {January 2020}, + volume = {12}, + number = {4}, + month = nov, + year = {2019}, + issn = {1556-4673}, + articleno = {29}, + numpages = {27}, + url = {https://doi.org/10.1145/3344383}, + doi = {10.1145/3344383}, + acmid = {}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, +} + +@inproceedings{AMASS_DFaust, + title = {Dynamic {FAUST}: {R}egistering Human Bodies in Motion}, + author = {Bogo, Federica and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.}, + booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, + month = jul, + year = {2017}, + month_numeric = {7} +} + +@misc{AMASS_EyesJapanDataset, + title = {{Eyes Japan MoCap Dataset}}, + author = {Eyes JAPAN Co. Ltd.}, + url = {http://mocapdata.com} +} + +@inproceedings{AMASS_GRAB, + title = {{GRAB}: A Dataset of Whole-Body Human Grasping of Objects}, + author = {Taheri, Omid and Ghorbani, Nima and Black, Michael J. and Tzionas, Dimitrios}, + booktitle = {European Conference on Computer Vision (ECCV)}, + year = {2020}, + url = {https://grab.is.tue.mpg.de} +} + +@inproceedings{AMASS_GRAB-2, + title = {{ContactDB}: Analyzing and Predicting Grasp Contact via Thermal Imaging}, + author = {Brahmbhatt, Samarth and Ham, Cusuh and Kemp, Charles C. and Hays, James}, + booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2019}, + url = {https://contactdb.cc.gatech.edu} +} + +@techreport{AMASS_HDM05, + author = {M. M\"{u}ller and T. R\"{o}der and M. Clausen and B. Eberhardt and B. Kr\"{u}ger and A. Weber}, + title = {Documentation Mocap Database HDM05}, + number = {CG-2007-2}, + year = {2007}, + month = {June}, + institution = {Universit\"{a}t Bonn}, + issn = {1610-8892} +} + +@article{AMASS_HUMAN4D, + title = {HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media}, + author = {Chatzitofis, Anargyros and Saroglou, Leonidas and Boutis, Prodromos and Drakoulis, Petros and Zioulis, Nikolaos and Subramanyam, Shishir and Kevelham, Bart and Charbonnier, Caecilia and Cesar, Pablo and Zarpalas, Dimitrios and others}, + journal = {IEEE Access}, + volume = {8}, + pages = {176241--176262}, + year = {2020}, + publisher = {IEEE} +} + +@article{AMASS_HumanEva, + title = {{HumanEva}: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion}, + author = {Sigal, L. and Balan, A. and Black, M. J.}, + journal = {International Journal of Computer Vision}, + volume = {87}, + number = {1}, + pages = {4--27}, + publisher = {Springer Netherlands}, + month = mar, + year = {2010}, + doi = {}, + month_numeric = {3} +} + +@inproceedings{AMASS_KIT-CNRS-EKUT-WEIZMANN, + author = {Christian Mandery and \"Omer Terlemez and Martin Do and Nikolaus Vahrenkamp and Tamim Asfour}, + title = {The {KIT} Whole-Body Human Motion Database}, + booktitle = {International Conference on Advanced Robotics (ICAR)}, + pages = {329--336}, + year = {2015}, +} + +@article{AMASS_KIT-CNRS-EKUT-WEIZMANN-2, + author = {Christian Mandery and \"Omer Terlemez and Martin Do and Nikolaus Vahrenkamp and Tamim Asfour}, + title = {Unifying Representations and Large-Scale Whole-Body Motion Databases for Studying Human Motion}, + pages = {796--809}, + volume = {32}, + number = {4}, + journal = {IEEE Transactions on Robotics}, + year = {2016}, +} + +@inproceedings{AMASS_KIT-CNRS-EKUT-WEIZMANN-3, + author = {Franziska Krebs and Andre Meixner and Isabel Patzer and Tamim Asfour}, + title = {The {KIT} Bimanual Manipulation Dataset}, + booktitle = {IEEE/RAS International Conference on Humanoid Robots (Humanoids)}, + pages = {499--506}, + year = {2021}, +} + +@inproceedings{AMASS_MOYO, + title = {{3D} Human Pose Estimation via Intuitive Physics}, + author = {Tripathi, Shashank and M{\"u}ller, Lea and Huang, Chun-Hao P. and Taheri Omid and Black, Michael J. and Tzionas, Dimitrios}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2023} +} + +@article{AMASS_MoSh, + title = {{MoSh}: Motion and Shape Capture from Sparse Markers}, + author = {Loper, Matthew M. and Mahmood, Naureen and Black, Michael J.}, + address = {New York, NY, USA}, + publisher = {ACM}, + month = nov, + number = {6}, + volume = {33}, + pages = {220:1--220:13}, + abstract = {Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. We argue that important information about body surface motion is present in standard marker sets but is lost in extracting a skeleton. We demonstrate a new approach called MoSh (Motion and Shape capture), that automatically extracts this detail from mocap data. MoSh estimates body shape and pose together using sparse marker data by exploiting a parametric model of the human body. In contrast to previous work, MoSh solves for the marker locations relative to the body and estimates accurate body shape directly from the markers without the use of 3D scans; this effectively turns a mocap system into an approximate body scanner. MoSh is able to capture soft tissue motions directly from markers, by allowing body shape to vary over time. We evaluate the effect of different marker sets on pose and shape accuracy and propose a new sparse marker set for capturing soft-tissue motion. We illustrate MoSh by recovering body shape, pose, and soft-tissue motion from archival mocap data and using this to produce animations with subtlety and realism. We also show soft-tissue motion retargeting to new characters and show how to magnify the 3D deformations of soft tissue to create animations with appealing exaggerations.}, + journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)}, + url = {http://doi.acm.org/10.1145/2661229.2661273}, + year = {2014}, + doi = {10.1145/2661229.2661273} +} + +@inproceedings{AMASS_PosePrior, + title = {Pose-Conditioned Joint Angle Limits for {3D} Human Pose Reconstruction}, + author = {Akhter, Ijaz and Black, Michael J.}, + booktitle = { IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015}, + month = jun, + abstract = {The estimation of 3D human pose from 2D joint locations is central to many vision problems involving the analysis, of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collected a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. The dataset and the prior will be made publicly available. Second, we define a general parameterization of body pose and a new, multistage, method to estimate 3D pose from 2D joint locations that uses an over-complete dictionary of human poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results on manual annotations on real images and automatic part-based detections on the Leeds sports pose dataset.}, + year = {2015} +} + +@misc{AMASS_SFU, + title = {{SFU Motion Capture Database}}, + author = {Simon Fraser University and National University of Singapore}, + url = {http://mocap.cs.sfu.ca/} +} + +@inproceedings{AMASS_SOMA, + title = {{SOMA}: Solving Optical Marker-Based MoCap Automatically}, + author = {Ghorbani, Nima and Black, Michael J.}, + booktitle = {Proc. International Conference on Computer Vision (ICCV)}, + pages = {11117--11126}, + month = oct, + year = {2021}, + doi = {}, + month_numeric = {10} +} + +@inproceedings{AMASS_TCDHands, + author = {Ludovic Hoyet and Kenneth Ryall and Rachel McDonnell and Carol O'Sullivan}, + title = {Sleight of Hand: Perception of Finger Motion from Reduced Marker Sets}, + booktitle = {Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games}, + year = {2012}, + pages = {79--86}, + doi = {10.1145/2159616.2159629} +} + +@inproceedings{AMASS_TotalCapture, + author = {Trumble, Matt and Gilbert, Andrew and Malleson, Charles and Hilton, Adrian and Collomosse, John}, + title = {{Total Capture}: 3D Human Pose Estimation Fusing Video and Inertial Sensors}, + booktitle = {2017 British Machine Vision Conference (BMVC)}, + year = {2017} +} + +@inproceedings{AMASS_WheelPoser, + title={WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users}, + author={Li, Yunzhi and Mollyn, Vimal and Yuan, Kuang and Carrington, Patrick}, + booktitle={Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility}, + pages={1--17}, + year={2024} +} \ No newline at end of file diff --git a/license.txt b/license.txt new file mode 100644 index 0000000000000000000000000000000000000000..3311a51145fa46abca58377c476c6e8f120f2da1 --- /dev/null +++ b/license.txt @@ -0,0 +1,50 @@ +Dataset Copyright License for non-commercial scientific research purposes +Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use the AMASS Dataset and the accompanying Software (jointly refered to as the "Dataset"). By downloading and/or using the Dataset, you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Dataset. Any infringement of the terms of this agreement will automatically terminate your rights under this License + +Ownership / Licensees +The Dataset and the associated materials has been developed at the Max Planck Institute for Intelligent Systems (hereinafter "MPI"). Any copyright or patent right is owned by and proprietary material of the Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (hereinafter “MPG”; MPI and MPG hereinafter collectively “Max-Planck”) hereinafter the “Licensor”. + +License Grant +Licensor grants you (Licensee) personally a single-user, non-exclusive, non-transferable, free of charge right: + +To obtain and install the Dataset on computers owned, leased or otherwise controlled by you and/or your organization; +To use the Dataset for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects; +Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes. The Dataset may not be reproduced, modified and/or made available in any form to any third party without Max-Planck’s prior written permission. + +The Dataset may not be used for pornographic purposes or to generate pornographic material whether commercial or not. This license also prohibits the use of the Dataset to train methods/algorithms/neural networks/etc. for commercial use of any kind. By downloading the Dataset, you agree not to reverse engineer it. + +No Distribution +The Dataset and the license herein granted shall not be copied, shared, distributed, re-sold, offered for re-sale, transferred or sub-licensed in whole or in part except that you may make one copy for archive purposes only. + +Disclaimer of Representations and Warranties +You expressly acknowledge and agree that the Dataset results from basic research, is provided “AS IS”, may contain errors, and that any use of the Dataset is at your sole risk. LICENSOR MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE SOFTWARE, NEITHER EXPRESS NOR IMPLIED, AND THE ABSENCE OF ANY LEGAL OR ACTUAL DEFECTS, WHETHER DISCOVERABLE OR NOT. Specifically, and not to limit the foregoing, licensor makes no representations or warranties (i) regarding the merchantability or fitness for a particular purpose of the Dataset, (ii) that the use of the Dataset will not infringe any patents, copyrights or other intellectual property rights of a third party, and (iii) that the use of the Dataset will not cause any damage of any kind to you or a third party. + +Limitation of Liability +Because this Software License Agreement qualifies as a donation, according to Section 521 of the German Civil Code (Bürgerliches Gesetzbuch – BGB) Licensor as a donor is liable for intent and gross negligence only. If the Licensor fraudulently conceals a legal or material defect, they are obliged to compensate the Licensee for the resulting damage. +Licensor shall be liable for loss of data only up to the amount of typical recovery costs which would have arisen had proper and regular data backup measures been taken. For the avoidance of doubt Licensor shall be liable in accordance with the German Product Liability Act in the event of product liability. The foregoing applies also to Licensor’s legal representatives or assistants in performance. Any further liability shall be excluded. +Patent claims generated through the usage of the Dataset cannot be directed towards the copyright holders. +The contractor points out that add-ons as well as minor modifications to the Dataset may lead to unforeseeable and considerable disruptions. + +No Maintenance Services +You understand and agree that Licensor is under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Dataset. Licensor nevertheless reserves the right to update, modify, or discontinue the Dataset at any time. + +Defects of the Dataset must be notified in writing to the Licensor with a comprehensible description of the error symptoms. The notification of the defect should enable the reproduction of the error. The Licensee is encouraged to communicate any use, results, modification or publication. + +Publications using the Dataset +You acknowledge that the Dataset is a valuable scientific resource and agree to appropriately reference the following paper in any publication making use of the Dataset. + +Citation: + +@inproceedings{AMASS:2019, + title={AMASS: Archive of Motion Capture as Surface Shapes}, + author={Mahmood, Naureen and Ghorbani, Nima and F. Troje, Nikolaus and Pons-Moll, Gerard and Black, Michael J.}, + booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, + year={2019}, + month = {Oct}, + url = {https://amass.is.tue.mpg.de}, + month_numeric = {10} +} +Commercial licensing opportunities +For commercial uses of the Dataset, please send email to ps-license@tue.mpg.de + +This Agreement shall be governed by the laws of the Federal Republic of Germany except for the UN Sales Convention. \ No newline at end of file diff --git a/visualize.py b/visualize.py new file mode 100644 index 0000000000000000000000000000000000000000..cd938c1ab796c700aeedf7bd176fba2d8d832feb --- /dev/null +++ b/visualize.py @@ -0,0 +1,53 @@ +import numpy as np +import time +import mujoco +import mujoco.viewer +from robot_descriptions.loaders.mujoco import load_robot_description +''' +pip install git+https://github.com/robot-descriptions/robot_descriptions.py.git@main +''' + +def mj_play(data, fr): + m = load_robot_description("elf2_mj_description") # load robot description like python module + # or load from local xml file + # m = mujoco.MjModel.from_xml_path('path/to/elf2_dof25.xml') + m.opt.timestep = 1.0/fr + d = mujoco.MjData(m) + + len = data.shape[0] + step = 0 + + with mujoco.viewer.launch_passive(m, d, show_left_ui=False, show_right_ui=False) as viewer: + # set camera + viewer.cam.lookat = d.body("base_link").xpos # camera following "base_link" body + viewer.cam.distance = 5.0 + viewer.cam.elevation = -20 + #viewer.paused = True + + while viewer.is_running() and step 0: + time.sleep(time_until_next_step) + +def read_rtj(fpath): + #get frame rate from file name + fr = fpath[-12:-9] + fr = int(fr[1:]) if fr[0]=='_' else int(fr) + + jpos = np.load(fpath) + jpos[:,2] += 0.793 + return jpos,fr + +if __name__ == '__main__': + jpos, fr = read_rtj('./bxi_elf2/CMU/01/01_01_poses_120_jpos.npy') + mj_play(jpos, fr)