Abstract
Existing self-contact detection methods have difficulty detecting dense per-vertex self-contact. Dataset collection for existing self-contact detection methods is costly and inefficient, as it requires different subjects to mimic the same pose. In this paper, we propose a generation-to-generalization approach by utilizing ControlNet to augment existing datasets. Based on that we develop a keypoint-conditioned neural network that can successfully infer per-vertex self-contact from a single image. With the extended dataset synthesized by ControlNet, our network requires only one real subject training data to achieve satisfactory individual generalization ability. Experiments verify the effectiveness of our proposed method and the improvement of the network’s generalization with synthetic data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Video based reconstruction of 3d people models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8387–8397 (2018)
Bogo, F., Romero, J., Loper, M., Black, M.J.: Faust: dataset and evaluation for 3d mesh registration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3794–3801 (2014). https://doi.org/10.1109/CVPR.2014.491
Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2021)
Chen, Y., Dwivedi, S.K., Black, M.J., Tzionas, D.: Detecting human-object contact in images. arXiv preprint arXiv:2303.03373 pp. 17100–17110 (2023)
Fieraru, M., Zanfir, M., Oneata, E., Popa, A.I., Olaru, V., Sminchisescu, C.: Learning complex 3d human self-contact. Proc. AAAI Conf. Artif. Intell. 35, 1343–1351 (2021)
Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8359–8367 (June 2018)
Hassan, M., Choutas, V., Tzionas, D., Black, M.J.: Resolving 3d human pose ambiguities with 3d scene constraints. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2282–2292 (2019)
Hassan, M., Ghosh, P., Tesch, J., Tzionas, D., Black, M.J.: Populating 3d scenes by learning human-scene interaction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14708–14718 (2021)
Hu, Y.T., Chen, H.S., Hui, K., Huang, J.B., Schwing, A.G.: Sail-vos: semantic amodal instance level video object segmentation - a synthetic dataset and baselines. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3100–3110 (June 2019)
Huang, C.H.P., et al.: Capturing and inferring dense full-body human-scene contact. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13274–13285 (Jun 2022)
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)
Li, Q., Peng, Z., Zhang, Q., Qiu, C., Liu, C., Zhou, B.: Improving the generalization of end-to-end driving through procedural generation. arXiv preprint arXiv:2012.13681 (2020)
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: Smpl: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 248:1–248:16 (2015)
Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.: Amass: archive of motion capture as surface shapes. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5441–5450 (2019)
Müller, L., Osman, A.A.A., Tang, S., Huang, C.H.P., Black, M.J.: On self-contact and human pose. In: Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9990–9999 (Jun 2021)
Pavlakos, G., et al.: Expressive body capture: 3d hands, face, and body from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10975–10985 (2019)
Peng, S., et al.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9050–9059 (2021)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10674–10685 (2022)
Shimada, S., Golyanik, V., Li, Z., Pérez, P., Xu, W., Theobalt, C.: Hulc: 3d human motion capture with pose manifold sampling and dense contact guidance. In: Proceedings of the European Conference on Computer Vision, pp. 516–533 (Jun 2022)
Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 608–617 (2019). https://doi.org/10.1109/CVPR.2019.00070
Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349–3364 (2020)
Yu, T., Zheng, Z., Guo, K., Liu, P., Dai, Q., Liu, Y.: Function4d: real-time human volumetric capture from very sparse consumer RGBD sensors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5742–5752 (June 2021)
Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, H., Zhao, J., Li, F., Tan, C., Sun, S. (2024). Robust Self-contact Detection Based on Keypoint Condition and ControlNet-Based Augmentation. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14473. Springer, Singapore. https://doi.org/10.1007/978-981-99-8850-1_6
Download citation
DOI: https://doi.org/10.1007/978-981-99-8850-1_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8849-5
Online ISBN: 978-981-99-8850-1
eBook Packages: Computer ScienceComputer Science (R0)