{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:13:25Z","timestamp":1769746405333,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819988495","type":"print"},{"value":"9789819988501","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8850-1_6","type":"book-chapter","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T18:02:05Z","timestamp":1706983325000},"page":"66-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Self-contact Detection Based on\u00a0Keypoint Condition and\u00a0ControlNet-Based Augmentation"],"prefix":"10.1007","author":[{"given":"He","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jianhui","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Shuangpeng","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"6_CR1","unstructured":"https:\/\/developer.nvidia.com\/zh-cn\/drive\/drive-sim"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"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\u20138397 (2018)","DOI":"10.1109\/CVPR.2018.00875"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"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\u20133801 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.491","DOI":"10.1109\/CVPR.2014.491"},{"issue":"1","key":"6_CR4","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"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\u2013186 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dwivedi, S.K., Black, M.J., Tzionas, D.: Detecting human-object contact in images. arXiv preprint arXiv:2303.03373 pp. 17100\u201317110 (2023)","DOI":"10.1109\/CVPR52729.2023.01640"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"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\u20131351 (2021)","DOI":"10.1609\/aaai.v35i2.16223"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Gkioxari, G., Girshick, R., Doll\u00e1r, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8359\u20138367 (June 2018)","DOI":"10.1109\/CVPR.2018.00872"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"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\u20132292 (2019)","DOI":"10.1109\/ICCV.2019.00237"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"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\u201314718 (2021)","DOI":"10.1109\/CVPR46437.2021.01447"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"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\u20133110 (June 2019)","DOI":"10.1109\/CVPR.2019.00322"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"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\u201313285 (Jun 2022)","DOI":"10.1109\/CVPR52688.2022.01292"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"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\u20131339 (2014)","DOI":"10.1109\/TPAMI.2013.248"},{"key":"6_CR13","unstructured":"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)"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"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\u2013248:16 (2015)","DOI":"10.1145\/2816795.2818013"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"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\u20135450 (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"M\u00fcller, 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\u20139999 (Jun 2021)","DOI":"10.1109\/CVPR46437.2021.00986"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"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\u201310985 (2019)","DOI":"10.1109\/CVPR.2019.01123"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"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\u20139059 (2021)","DOI":"10.1109\/CVPR46437.2021.00894"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"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\u201310685 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Shimada, S., Golyanik, V., Li, Z., P\u00e9rez, 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\u2013533 (Jun 2022)","DOI":"10.1007\/978-3-031-20047-2_30"},{"key":"6_CR21","doi-asserted-by":"publisher","unstructured":"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\u2013617 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00070","DOI":"10.1109\/CVPR.2019.00070"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","DOI":"10.1109\/TPAMI.2020.2983686"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"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\u20135752 (June 2021)","DOI":"10.1109\/CVPR46437.2021.00569"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models (2023)","DOI":"10.1109\/ICCV51070.2023.00355"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8850-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T18:03:07Z","timestamp":1706983387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8850-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819988495","9789819988501"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8850-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"376","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}