{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:50:45Z","timestamp":1743036645654,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984312"},{"type":"electronic","value":"9789819984329"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"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\/978-981-99-8432-9_5","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T08:02:17Z","timestamp":1703318537000},"page":"54-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FIRE: Fine Implicit Reconstruction Enhancement with\u00a0Detailed Body Part Labels and\u00a0Geometric Features"],"prefix":"10.1007","author":[{"given":"Junzheng","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xipeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Keze","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pengxu","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Alldieck, T., Zanfir, M., Sminchisescu, C.: Photorealistic monocular 3d reconstruction of humans wearing clothing. In: CVPR, pp. 1506\u20131515 (2022)","DOI":"10.1109\/CVPR52688.2022.00156"},{"key":"5_CR2","first-page":"17373","volume":"35","author":"K Chan","year":"2022","unstructured":"Chan, K., Lin, G., Zhao, H., Lin, W.: S-pifu: integrating parametric human models with pifu for single-view clothed human reconstruction. Adv. Neural. Inf. Process. Syst. 35, 17373\u201317385 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Chan, K.Y., Lin, G., Zhao, H., Lin, W.: Integratedpifu: integrated pixel aligned implicit function for single-view human reconstruction. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part II, pp. 328\u2013344. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_19","DOI":"10.1007\/978-3-031-20086-1_19"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Zheng, Y., Black, M.J., Hilliges, O., Geiger, A.: Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11594\u201311604 (2021)","DOI":"10.1109\/ICCV48922.2021.01139"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Kavan, L., Collins, S., \u017d\u00e1ra, J., O\u2019Sullivan, C.: Skinning with dual quaternions. In: Proceedings of the 2007 Symposium on Interactive 3D Graphics and Games, pp. 39\u201346 (2007)","DOI":"10.1145\/1230100.1230107"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: CVPR, pp. 5939\u20135948 (2019)","DOI":"10.1109\/CVPR.2019.00609"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Feng, Q., Liu, Y., Lai, Y.K., Yang, J., Li, K.: Fof: learning fourier occupancy field for monocular real-time human reconstruction. arXiv:2206.02194 (2022)","DOI":"10.1007\/978-3-031-20503-3_54"},{"key":"5_CR8","first-page":"9276","volume":"33","author":"T He","year":"2020","unstructured":"He, T., Collomosse, J., Jin, H., Soatto, S.: Geo-pifu: geometry and pixel aligned implicit functions for single-view human reconstruction. Adv. Neural. Inf. Process. Syst. 33, 9276\u20139287 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"He, T., Xu, Y., Saito, S., Soatto, S., Tung, T.: Arch++: animation-ready clothed human reconstruction revisited. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11046\u201311056 (2021)","DOI":"10.1109\/ICCV48922.2021.01086"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: One-shot implicit animatable avatars with model-based priors. arXiv:2212.02469 (2022)","DOI":"10.1109\/ICCV51070.2023.00824"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xu, Y., Lassner, C., Li, H., Tung, T.: Arch: animatable reconstruction of clothed humans. In: CVPR, pp. 3093\u20133102 (2020)","DOI":"10.1109\/CVPR42600.2020.00316"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, B., Hong, Y., Bao, H., Zhang, J.: Selfrecon: self reconstruction your digital avatar from monocular video. In: CVPR, pp. 5605\u20135615 (2022)","DOI":"10.1109\/CVPR52688.2022.00552"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Joo, H., Simon, T., Sheikh, Y.: Total capture: a 3d deformation model for tracking faces, hands, and bodies. In: CVPR, pp. 8320\u20138329 (2018)","DOI":"10.1109\/CVPR.2018.00868"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Lin, S., Zhang, H., Zheng, Z., Shao, R., Liu, Y.: Learning implicit templates for point-based clothed human modeling. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part III, pp. 210\u2013228. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20062-5_13","DOI":"10.1007\/978-3-031-20062-5_13"},{"issue":"6","key":"5_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","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), 1\u201316 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"5_CR16","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. ACM siggraph computer graphics 21(4), 163\u2013169 (1987)","journal-title":"ACM siggraph computer graphics"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Ma, Q., et al.: Learning to dress 3d people in generative clothing. In: CVPR, pp. 6469\u20136478 (2020)","DOI":"10.1109\/CVPR42600.2020.00650"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: CVPR, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"issue":"1","key":"5_CR19","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: CVPR, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Pavlakos, G., et al.: Expressive body capture: 3d hands, face, and body from a single image. In: CVPR, pp. 10975\u201310985 (2019)","DOI":"10.1109\/CVPR.2019.01123"},{"key":"5_CR22","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: CVPR, pp. 9054\u20139063 (2021)","DOI":"10.1109\/CVPR46437.2021.00894"},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Pesavento, M., Volino, M., Hilton, A.: Super-resolution 3d human shape from a single low-resolution image. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part II, pp. 447\u2013464. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_26","DOI":"10.1007\/978-3-031-20086-1_26"},{"key":"5_CR24","unstructured":"Romero, J., Tzionas, D., Black, M.J.: Embodied hands: modeling and capturing hands and bodies together. arXiv:2201.02610 (2022)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: Pifu: pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2304\u20132314 (2019)","DOI":"10.1109\/ICCV.2019.00239"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Saito, S., Simon, T., Saragih, J., Joo, H.: Pifuhd: multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In: CVPR, pp. 84\u201393 (2020)","DOI":"10.1109\/CVPR42600.2020.00016"},{"key":"5_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-030-11018-5_6","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"AS Jackson","year":"2019","unstructured":"Jackson, A.S., Manafas, C., Tzimiropoulos, G.: 3D human body reconstruction from a single image via volumetric regression. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11132, pp. 64\u201377. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11018-5_6"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Wang, K., Zheng, H., Zhang, G., Yang, J.: Parametric model estimation for 3d clothed humans from point clouds. In: ISMAR, pp. 156\u2013165. IEEE (2021)","DOI":"10.1109\/ISMAR52148.2021.00030"},{"key":"5_CR29","first-page":"2810","volume":"34","author":"S Wang","year":"2021","unstructured":"Wang, S., Mihajlovic, M., Ma, Q., Geiger, A., Tang, S.: Metaavatar: learning animatedly clothed human models from few depth images. Adv. Neural. Inf. Process. Syst. 34, 2810\u20132822 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Weng, C.Y., Curless, B., Srinivasan, P.P., Barron, J.T., Kemelmacher-Shlizerman, I.: Humannerf: free-viewpoint rendering of moving people from monocular video. In: CVPR, pp. 16210\u201316220 (2022)","DOI":"10.1109\/CVPR52688.2022.01573"},{"key":"5_CR31","unstructured":"Xiong, Z., et al.: Pifu for the real world: a self-supervised framework to reconstruct dressed humans from single-view images. arXiv:2208.10769 (2022)"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Xiu, Y., Yang, J., Tzionas, D., Black, M.J.: Icon: implicit clothed humans obtained from normals. In: CVPR, pp. 13286\u201313296. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.01294"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Xu, H., Bazavan, E.G., Zanfir, A., Freeman, W.T., Sukthankar, R., Sminchisescu, C.: Ghum & ghuml: generative 3d human shape and articulated pose models. In: CVPR, pp. 6184\u20136193 (2020)","DOI":"10.1109\/CVPR42600.2020.00622"},{"issue":"6","key":"5_CR34","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1109\/TPAMI.2021.3050505","volume":"44","author":"Z Zheng","year":"2021","unstructured":"Zheng, Z., Yu, T., Liu, Y., Dai, Q.: Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3170\u20133184 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Patel, P., Huang, C.H.P., Tesch, J., Hoffmann, D.T., Tripathi, S., Black, M.J.: Agora: avatars in geography optimized for regression analysis. In: CVPR, pp. 13468\u201313478 (2021)","DOI":"10.1109\/CVPR46437.2021.01326"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: Deephuman: 3d human reconstruction from a single image. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 7739\u20137749 (2019)","DOI":"10.1109\/ICCV.2019.00783"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Moon, G., Nam, H., Shiratori, T., Lee, K.M.: 3d clothed human reconstruction in the wild. In: Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Proceedings, Part II, pp. 184\u2013200. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-20086-1_11","DOI":"10.1007\/978-3-031-20086-1_11"},{"issue":"4","key":"5_CR38","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1007\/s11263-022-01736-z","volume":"131","author":"SS Jinka","year":"2023","unstructured":"Jinka, S.S., Srivastava, A., Pokhariya, C., Sharma, A., Narayanan, P.: Sharp: shape-aware reconstruction of people in loose clothing. Int. J. Comput. Vision 131(4), 918\u2013937 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"5_CR39","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: CVPR, pp. 5746\u20135756 (2021)","DOI":"10.1109\/CVPR46437.2021.00569"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Huang, C.H.P., Hilliges, O., Black, M.J.: Pare: part attention regressor for 3d human body estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11127\u201311137 (2021)","DOI":"10.1109\/ICCV48922.2021.01094"},{"key":"5_CR41","unstructured":"Ravi, N., Reizenstein, J., Novotny, D., Gordon, T., Lo, W.Y., Johnson, J., Gkioxari, G.: Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501 (2020)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8432-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T19:32:56Z","timestamp":1730921576000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8432-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984312","9789819984329"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8432-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","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":"532","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":"0","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}