{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:17:54Z","timestamp":1792790274894,"version":"4.5.0"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197864","type":"print"},{"value":"9783031197871","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-19787-1_39","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T18:16:11Z","timestamp":1666289771000},"page":"685-701","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Intelli-Paint: Towards Developing More Human-Intelligible Painting Agents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2531-7878","authenticated-orcid":false,"given":"Jaskirat","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1815-1707","authenticated-orcid":false,"given":"Cameron","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6802-0911","authenticated-orcid":false,"given":"Jose","family":"Echevarria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1464-9500","authenticated-orcid":false,"given":"Liang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"39_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223. PMLR (2017)"},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Bangham, J.A., Gibson, S.E., Harvey, R.W.: The art of scale-space. In: BMVC. pp. 1\u201310. Citeseer (2003)","DOI":"10.5244\/C.17.58"},{"key":"39_CR3","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Chen, W., Hays, J.: SketchyGAN: towards diverse and realistic sketch to image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9416\u20139425 (2018)","DOI":"10.1109\/CVPR.2018.00981"},{"key":"39_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-540-32003-6_44","volume-title":"Applications of Evolutionary Computing","author":"JP Collomosse","year":"2005","unstructured":"Collomosse, J.P., Hall, P.M.: Genetic paint: a search for salient paintings. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 437\u2013447. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-32003-6_44"},{"issue":"04","key":"39_CR6","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1142\/S0218213006002813","volume":"15","author":"JP Collomosse","year":"2006","unstructured":"Collomosse, J.P., Hall, P.M.: Salience-adaptive painterly rendering using genetic search. Int. J. Artif. Intell. Tools 15(04), 551\u2013575 (2006)","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"39_CR7","doi-asserted-by":"crossref","unstructured":"Collomosse, J., Hall, P.: Painterly rendering using image salience. In: Proceedings 20th Eurographics UK Conference, pp. 122\u2013128. IEEE (2002)","DOI":"10.1109\/EGUK.2002.1011281"},{"key":"39_CR8","unstructured":"Frans, K., Cheng, C.Y.: Unsupervised image to sequence translation with canvas-drawer networks. arXiv preprint arXiv:1809.08340 (2018)"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Frans, K., Soros, L., Witkowski, O.: Clipdraw: exploring text-to-drawing synthesis through language-image encoders. arXiv preprint arXiv:2106.14843 (2021)","DOI":"10.52202\/068431-0376"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Gagniuc, P.A.: Markov Chains: From Theory to Implementation and Experimentation. John Wiley & Sons, Hoboken (2017)","DOI":"10.1002\/9781119387596"},{"key":"39_CR11","unstructured":"Ganin, Y., Kulkarni, T., Babuschkin, I., Eslami, S., Vinyals, O.: Synthesizing programs for images using reinforced adversarial learning. arXiv preprint arXiv:1804.01118 (2018)"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"39_CR13","unstructured":"Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)"},{"key":"39_CR14","unstructured":"Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2017)"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Haeberli, P.: Paint by numbers: abstract image representations. In: Proceedings of the 17th Annual Conference on Computer Graphics and Interactive Techniques, pp. 207\u2013214 (1990)","DOI":"10.1145\/97879.97902"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 453\u2013460 (1998)","DOI":"10.1145\/280814.280951"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Hertzmann, A., et al.: A survey of stroke-based rendering. IEEE Comput. Graph. Appl. 23, 70\u201381. Institute of Electrical and Electronics Engineers (2003)","DOI":"10.1109\/MCG.2003.1210867"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Huang, Z., Heng, W., Zhou, S.: Learning to paint with model-based deep reinforcement learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8709\u20138718 (2019)","DOI":"10.1109\/ICCV.2019.00880"},{"key":"39_CR19","unstructured":"Jia, B., Brandt, J., Mech, R., Kim, B., Manocha, D.: LPaintB: learning to paint from self-supervision arXiv preprint arXiv:1906.06841 (2019)"},{"key":"39_CR20","doi-asserted-by":"publisher","unstructured":"Jocher, G., et al.: ultralytics\/yolov5: v6.0 - YOLOv5n \u2019Nano\u2019 models, Roboflow integration, TensorFlow export, OpenCV DNN support, October 2021. https:\/\/doi.org\/10.5281\/zenodo.5563715","DOI":"10.5281\/zenodo.5563715"},{"key":"39_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time Style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"39_CR22","unstructured":"Kite-Powell, J.: This AI robot will paint a canvas at sxsw 2021 (March 2021), https:\/\/www.forbes.com\/sites\/jenniferhicks\/2021\/03\/10\/this-ai-robot-will-paint-a-canvas-at-sxsw-2021\/?sh=5b1f0d1ab449"},{"key":"39_CR23","doi-asserted-by":"crossref","unstructured":"Kotovenko, D., Wright, M., Heimbrecht, A., Ommer, B.: Rethinking style transfer: from pixels to parameterized brushstrokes. arXiv preprint arXiv:2103.17185 (2021)","DOI":"10.1109\/CVPR46437.2021.01202"},{"key":"39_CR24","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13). Sydney, Australia (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"39_CR25","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Liu, Z., Wu, L., Luo, P.: MaskGAN: towards diverse and interactive facial image manipulation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00559"},{"key":"39_CR26","doi-asserted-by":"crossref","unstructured":"Litwinowicz, P.: Processing images and video for an impressionist effect. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 407\u2013414 (1997)","DOI":"10.1145\/258734.258893"},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Liu, B., Gould, S., Koller, D.: Single image depth estimation from predicted semantic labels. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1253\u20131260. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539823"},{"key":"39_CR28","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Paint transformer: feed forward neural painting with stroke prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6598\u20136607 (2021)","DOI":"10.1109\/ICCV48922.2021.00653"},{"key":"39_CR29","unstructured":"Mellor, J.F., et al.: Unsupervised doodling and painting with improved spiral. arXiv preprint arXiv:1910.01007 (2019)"},{"key":"39_CR30","unstructured":"Nakano, R.: Neural painters: a learned differentiable constraint for generating brushstroke paintings. arXiv preprint arXiv:1904.08410 (2019)"},{"key":"39_CR31","unstructured":"Nemire, B.: Ai painting robot, May 2017. https:\/\/developer.nvidia.com\/blog\/ai-painting-robot\/"},{"key":"39_CR32","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O., Jagersand, M.: U2-Net: going deeper with nested u-structure for salient object detection. Pattern Recogn. 106, p. 107404 (2020)","DOI":"10.1016\/j.patcog.2020.107404"},{"key":"39_CR33","unstructured":"Reyner, N.: How to paint with layers - in acrylic & oil, December 2017. https:\/\/nancyreyner.com\/2017\/12\/25\/what-is-layering-for-painting\/"},{"key":"39_CR34","doi-asserted-by":"crossref","unstructured":"Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local source image approximation. In: Proceedings of the 1st International Symposium on Non-photorealistic Animation and Rendering, pp. 53\u201358 (2000)","DOI":"10.1145\/340916.340923"},{"key":"39_CR35","doi-asserted-by":"crossref","unstructured":"Singh, J., Zheng, L.: Combining semantic guidance and deep reinforcement learning for generating human level paintings. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2021)","DOI":"10.1109\/CVPR46437.2021.01612"},{"key":"39_CR36","doi-asserted-by":"crossref","unstructured":"Singh, J., Zheng, L., Smith, C., Echevarria, J.: Paint2pix: interactive painting based progressive image synthesis and editing. In: European Conference on Computer Vision. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-19781-9_39"},{"issue":"6","key":"39_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478513.3480549","volume":"40","author":"\u0160 Sochorov\u00e1","year":"2021","unstructured":"Sochorov\u00e1, \u0160, Jamri\u0161ka, O.: Practical pigment mixing for digital painting. ACM Trans. Graph. 40(6), 1\u201311 (2021)","journal-title":"ACM Trans. Graph."},{"key":"39_CR38","doi-asserted-by":"crossref","unstructured":"Teece, D.: 3D painting for non-photorealistic rendering. In: ACM SIGGRAPH 98 Conference Abstracts and Applications, p. 248 (1998)","DOI":"10.1145\/280953.282210"},{"key":"39_CR39","unstructured":"Treavett, S., Chen, M.: Statistical techniques for the automated synthesis of non-photorealistic images. In: Proceedings of 15th Eurographics UK Conference, pp. 201\u2013210 (1997)"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Turk, G., Banks, D.: Image-guided streamline placement. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 453\u2013460 (1996)","DOI":"10.1145\/237170.237285"},{"key":"39_CR41","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)"},{"key":"39_CR42","unstructured":"Wikipedia contributors: Ai-da (robot) \u2013 Wikipedia, the free encyclopedia (2022). https:\/\/en.wikipedia.org\/w\/index.php?title=Ai-Da_(robot) &oldid=1070639724. Accessed 7 Mar 2022"},{"key":"39_CR43","doi-asserted-by":"crossref","unstructured":"Xiang, X., Liu, D., Yang, X., Zhu, Y., Shen, X., Allebach, J.P.: Adversarial open domain adaptation for sketch-to-photo synthesis. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1434\u20131444 (2022)","DOI":"10.1109\/WACV51458.2022.00102"},{"issue":"5","key":"39_CR44","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1587\/transinf.E96.D.1134","volume":"96","author":"N Xie","year":"2013","unstructured":"Xie, N., Hachiya, H., Sugiyama, M.: Artist agent: a reinforcement learning approach to automatic stroke generation in oriental ink painting. IEICE Trans. Inf. Syst. 96(5), 1134\u20131144 (2013)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"39_CR45","doi-asserted-by":"publisher","first-page":"8797","DOI":"10.1109\/TIP.2021.3120669","volume":"30","author":"S Yang","year":"2021","unstructured":"Yang, S., Wang, Z., Liu, J., Guo, Z.: Controllable sketch-to-image translation for robust face synthesis. IEEE Trans. Image Process. 30, 8797\u20138810 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"39_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1640443.1640445","volume":"29","author":"K Zeng","year":"2009","unstructured":"Zeng, K., Zhao, M., Xiong, C., Zhu, S.C.: From image parsing to painterly rendering. ACM Trans. Graph. 29(1), 1\u20132 (2009)","journal-title":"ACM Trans. Graph."},{"key":"39_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, A., Balakrishnan, G., Lewis, K.M., Durand, F., Guttag, J.V., Dalca, A.V.: Painting many pasts: Synthesizing time lapse videos of paintings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8435\u20138445 (2020)","DOI":"10.1109\/CVPR42600.2020.00846"},{"key":"39_CR48","unstructured":"Zheng, N., Jiang, Y., Huang, D.: Strokenet: A neural painting environment. In: International Conference on Learning Representations (2018)"},{"key":"39_CR49","doi-asserted-by":"crossref","unstructured":"Zou, Z., Shi, T., Qiu, S., Yuan, Y., Shi, Z.: Stylized neural painting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15689\u201315698 (2021)","DOI":"10.1109\/CVPR46437.2021.01543"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19787-1_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:09:06Z","timestamp":1792541346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-19787-1_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197864","9783031197871"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19787-1_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}