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COUCH: Towards Controllable Human-Chair Interactions

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Humans interact with an object in many different ways by making contact at different locations, creating a highly complex motion space that can be difficult to learn, particularly when synthesizing such human interactions in a controllable manner. Existing works on synthesizing human scene interaction focus on the high-level control of action but do not consider the fine-grained control of motion. In this work, we study the problem of synthesizing scene interactions conditioned on different contact positions on the object. As a testbed to investigate this new problem, we focus on human-chair interaction as one of the most common actions which exhibit large variability in terms of contacts. We propose a novel synthesis framework COUCH that plans ahead the motion by predicting contact-aware control signals of the hands, which are then used to synthesize contact-conditioned interactions. Furthermore, we contribute a large human-chair interaction dataset with clean annotations, the COUCH Dataset. Our method shows significant quantitative and qualitative improvements over existing methods for human-object interactions. More importantly, our method enables control of the motion through user-specified or automatically predicted contacts.

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References

  1. http://virtualhumans.mpi-inf.mpg.de/couch/

  2. https://www.treedys.com/

  3. Agisoft metashape. https://www.agisoft.com/

  4. Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. https://www.xsens.com/. Accessed 30 Sep 2010

  5. Aksan, E., Kaufmann, M., Hilliges, O.: Structured prediction helps 3D human motion modelling. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  6. Aliakbarian, S., Saleh, F.S., Salzmann, M., Petersson, L., Gould, S.: A stochastic conditioning scheme for diverse human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  7. Alldieck, T., Magnor, M., Bhatnagar, B.L., Theobalt, C., Pons-Moll, G.: Learning to reconstruct people in clothing from a single RGB camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  8. Bhatnagar, B.L., Sminchisescu, C., Theobalt, C., Pons-Moll, G.: Combining implicit function learning and parametric models for 3D human reconstruction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 311–329. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_19

    Chapter  Google Scholar 

  9. Bhatnagar, B.L., Xie, X., Petrov, I.A., Sminchisescu, C., Theobalt, C., Pons-Moll, G.: Behave: dataset and method for tracking human object interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

    Google Scholar 

  10. Brahmbhatt, S., Ham, C., Kemp, C.C., Hays, J.: ContactDB: analyzing and predicting grasp contact via thermal imaging, cVPR (2019)

    Google Scholar 

  11. Cao, Z., Gao, H., Mangalam, K., Cai, Q.-Z., Vo, M., Malik, J.: Long-term human motion prediction with scene context. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 387–404. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_23

    Chapter  Google Scholar 

  12. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  13. Chang, A.X., et al.: Shapenet: an information-rich 3D model repository. CoRR abs/1512.03012 (2015)

    Google Scholar 

  14. Chao, Y., Yang, J., Chen, W., Deng, J.: Learning to sit: synthesizing human-chair interactions via hierarchical control. CoRR abs/1908.07423 (2019)

    Google Scholar 

  15. Corona, E., Pumarola, A., Alenyà, G., Moreno-Noguer, F.: Context-aware human motion prediction. CoRR abs/1904.03419 (2019)

    Google Scholar 

  16. Corona, E., Pumarola, A., Alenya, G., Moreno-Noguer, F., Rogez, G.: Ganhand: predicting human grasp affordances in multi-object scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  17. Cui, Q., Sun, H., Yang, F.: Learning dynamic relationships for 3D human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  18. Eigen, D., Ranzato, M., Sutskever, I.: Learning factored representations in a deep mixture of experts. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14–16 April 2014, Workshop Track Proceedings (2014)

    Google Scholar 

  19. Ghosh, P., Song, J., Aksan, E., Hilliges, O.: Learning human motion models for long-term predictions. In: s International Conference on 3D Vision 3DV (2017)

    Google Scholar 

  20. Gui, L.-Y., Wang, Y.-X., Liang, X., Moura, J.M.F.: Adversarial geometry-aware human motion prediction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 823–842. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_48

    Chapter  Google Scholar 

  21. Gui, L.-Y., Wang, Y.-X., Ramanan, D., Moura, J.M.F.: Few-shot human motion prediction via meta-learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 441–459. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01237-3_27

    Chapter  Google Scholar 

  22. Guzov, V., Mir, A., Sattler, T., Pons-Moll, G.: Human poseitioning system (HPS): 3D human pose estimation and self-localization in large scenes from body-mounted sensors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2021)

    Google Scholar 

  23. Guzov, V., Sattler, T., Pons-Moll, G.: Visually plausible human-object interaction capture from wearable sensors. arXiv (2022)

    Google Scholar 

  24. Habibie, I., Holden, D., Schwarz, J., Yearsley, J., Komura, T.: A recurrent variational autoencoder for human motion synthesis. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 119.1-119.12. BMVA Press (2017)

    Google Scholar 

  25. Hassan, M., et al.: Stochastic scene-aware motion prediction. In: Proceedings of the International Conference on Computer Vision 2021 (2021)

    Google Scholar 

  26. Hassan, M., Choutas, V., Tzionas, D., Black, M.J.: Resolving 3D human pose ambiguities with 3D scene constraints. In: International Conference on Computer Vision, pp. 2282–2292 (2019)

    Google Scholar 

  27. Henter, G.E., Alexanderson, S., Beskow, J.: Moglow: probabilistic and controllable motion synthesis using normalising flows. ACM Trans. Graph. 39(6), 236:1-236:14 (2020)

    Article  Google Scholar 

  28. Hernandez, A., Gall, J., Moreno-Noguer, F.: Human motion prediction via spatio-temporal inpainting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  29. Holden, D., Kanoun, O., Perepichka, M., Popa, T.: Learned motion matching. ACM Trans. Graph. 39(4), 53 (2020)

    Article  Google Scholar 

  30. Holden, D., Komura, T., Saito, J.: Phase-functioned neural networks for character control. ACM Trans. Graph. 36(4), 42:1-42:13 (2017)

    Article  Google Scholar 

  31. Holden, D., Saito, J., Komura, T.: A deep learning framework for character motion synthesis and editing. ACM Trans. Graph. 35(4), 1–11 (2016)

    Article  Google Scholar 

  32. Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014)

    Article  Google Scholar 

  33. Jiang, J., Streli, P., Fender, A., Qiu, H., Laich, L., Snape, P., Holz, C.: Avatarposer: Articulated full-body pose tracking from sparse motion sensing. In: European Conference on Computer Vision (ECCV) (2022)

    Google Scholar 

  34. Juliani, A., et al.: Unity: a general platform for intelligent agents. CoRR abs/1809.02627 (2018)

    Google Scholar 

  35. Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.: Grasping field: learning implicit representations for human grasps. In: International Conference on 3D Vision (3DV) (2020)

    Google Scholar 

  36. Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: dynamic multiscale graph neural networks for 3D skeleton based human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  37. Li, R., Yang, S., Ross, D.A., Kanazawa, A.: Ai choreographer: music conditioned 3D dance generation with aist++ (2021)

    Google Scholar 

  38. Li, X., Liu, S., Kim, K., Wang, X., Yang, M.H., Kautz, J.: Putting humans in a scene: learning affordance in 3D indoor environments. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  39. Liao, Z., Yang, J., Saito, J., Pons-Moll, G., Zhou, Y.: Skeleton-free pose transfer for stylized 3D characters. In: European Conference on Computer Vision (ECCV). Springer (2022)

    Google Scholar 

  40. Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR. IEEE Computer Society (2017)

    Google Scholar 

  41. Ling, H.Y., Zinno, F., Cheng, G., van de Panne, M.: Character controllers using motion vaes. ACM Trans. Graph. 39(4) (2020)

    Google Scholar 

  42. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34(6), 2481–24816 (2015)

    Google Scholar 

  43. Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: archive of motion capture as surface shapes. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October – 2 November, 2019

    Google Scholar 

  44. Mao, W., Liu, M., Salzmann, M., Li, H.: Learning trajectory dependencies for human motion prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  45. Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017

    Google Scholar 

  46. Nie, Y., Dai, A., Han, X., Nießner, M.: Pose2room: understanding 3D scenes from human activities. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022)

    Google Scholar 

  47. Pavllo, D., Grangier, D., Auli, M.: Quaternet: a quaternion-based recurrent model for human motion. In: British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  48. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  49. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497 (2015)

    Google Scholar 

  50. Rong, Y., Shiratori, T., Joo, H.: Frankmocap: a monocular 3D whole-body pose estimation system via regression and integration. In: IEEE International Conference on Computer Vision Workshops (2021)

    Google Scholar 

  51. Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 7–12 December 2015, pp. 3483–3491, Montreal, Quebec, Canada (2015)

    Google Scholar 

  52. Starke, S., Zhang, H., Komura, T., Saito, J.: Neural state machine for character-scene interactions. ACM Trans. Graph. 38(6), 209:1-209:14 (2019)

    Article  Google Scholar 

  53. Starke, S., Zhao, Y., Komura, T., Zaman, K.A.: Local motion phases for learning multi-contact character movements. ACM Trans. Graph. 9(4), 1–54 (2020)

    Google Scholar 

  54. Starke, S., Zhao, Y., Zinno, F., Komura, T.: Neural animation layering for synthesizing martial arts movements. ACM Trans. Graph. 40(4), 1–16 (2021)

    Article  Google Scholar 

  55. Taheri, O., Choutas, V., Black, M.J., Tzionas, D.: GOAL: generating 4D whole-body motion for hand-object grasping. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  56. Taheri, O., Ghorbani, N., Black, M.J., Tzionas, D.: GRAB: a dataset of whole-body human grasping of objects. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 581–600. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_34

    Chapter  Google Scholar 

  57. Wang, H., Feng, J.: VRED: A position-velocity recurrent encoder-decoder for human motion prediction. CoRR abs/1906.06514 (2019)

    Google Scholar 

  58. Wang, J., Xu, H., Xu, J., Liu, S., Wang, X.: Synthesizing long-term 3D human motion and interaction in 3D scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, 19–25 June 2021, pp. 9401–9411. Computer Vision Foundation / IEEE (2021)

    Google Scholar 

  59. Xie, X., Bhatnagar, B.L., Pons-Moll, G.: Chore: contact, human and object reconstruction from a single RGB image. In: European Conference on Computer Vision (ECCV). Springer (2022)

    Google Scholar 

  60. Xu, J., Xu, H., Ni, B., Yang, X., Wang, X., Darrell, T.: Hierarchical style-based networks for motion synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J. (eds.) Computer Vision - ECCV 2020–16th European Conference, Glasgow, UK, 23–28 August 2020, Proceedings, Part XI

    Google Scholar 

  61. Yi, H., et al.: Human-aware object placement for visual environment reconstruction. In: Computer Vision and Pattern Recognition (CVPR) (2022)

    Google Scholar 

  62. Yuan, Y., Kitani, K.: DLow: diversifying latent flows for diverse human motion prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 346–364. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_20

    Chapter  Google Scholar 

  63. Zhang, H., Starke, S., Komura, T., Saito, J.: Mode-adaptive neural networks for quadruped motion control. ACM Trans. Graph. 37(4), 145:1-145:11 (2018)

    Article  Google Scholar 

  64. Zhang, S., Zhang, Y., Ma, Q., Black, M.J., Tang, S.: PLACE: proximity learning of articulation and contact in 3D environments. In: International Conference on 3D Vision (3DV) (2020)

    Google Scholar 

  65. Zhang, Y., Hassan, M., Neumann, H., Black, M.J., Tang, S.: Generating 3D people in scenes without people. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  66. Zhou, K., Bhatnagar, B.L., Lenssen, J.E., Pons-Moll, G.: Toch: spatio-temporal object correspondence to hand for motion refinement. In: European Conference on Computer Vision (ECCV). Springer (2022)

    Google Scholar 

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Acknowledgement

This work was supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS18039A. This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 409792180 (Emmy Noether Programme, project: Real Virtual Humans). Gerard Pons-Moll is a member of the Machine Learning Cluster of Excellence, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645. We would like to thank Xianghui Xie for assisting the data processing, and we are grateful for all the participants involved in the data capture.

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Zhang, X., Bhatnagar, B.L., Starke, S., Guzov, V., Pons-Moll, G. (2022). COUCH: Towards Controllable Human-Chair Interactions. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_30

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