{"type":"ScholarlyArticle","title":"Semantic-Aware Generation of Multi-View Portrait Drawings","authors":[{"name":"Biao Ma","slug":"biao-ma","given":"Biao","family":"Ma","affiliation":"School of Computer Science and Technology , Hangzhou Dianzi University"},{"name":"Fei Gao","slug":"fei-gao","given":"Fei","family":"Gao","affiliation":"Hangzhou Institute of Technology , Xidian University"},{"name":"Chang Jiang","slug":"chang-jiang","given":"Chang","family":"Jiang","affiliation":"School of Computer Science and Technology , Hangzhou Dianzi University"},{"name":"Nannan Wang","slug":"nannan-wang","given":"Nannan","family":"Wang","affiliation":"ISN State Key Laboratory , Xidian University"},{"name":"Gang Xu","slug":"gang-xu","given":"Gang","family":"Xu","affiliation":"School of Computer Science and Technology , Hangzhou Dianzi University"}],"abstract":"Neural radiance fields (NeRF) based methods have shown amazing performance in synthesizing 3D-consistent photographic images, but fail to generate multi-view portrait drawings. The key is that the basic assumption of these methods -- a surface point is consistent when rendered from different views -- doesn't hold for drawings. In a portrait drawing, the appearance of a facial point may changes when viewed from different angles. Besides, portrait drawings usually present little 3D information and suffer from insufficient training data. To combat this challenge, in this paper, we propose a Semantic-Aware GEnerator (SAGE) for synthesizing multi-view portrait drawings. Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques. We therefore propose to collaboratively synthesize multi-view semantic maps and the corresponding portrait drawings. To facilitate training, we design a semantic-aware domain translator, which generates portrait drawings based on features of photographic faces. In addition, use data augmentation via synthesis to mitigate collapsed results. We apply SAGE to synthesize multi-view portrait drawings in diverse artistic styles. Experimental results show that SAGE achieves significantly superior or highly competitive performance, compared to existing 3D-aware image synthesis methods. The codes are available at https://github.com/AiArt-HDU/SAGE.","doi":"10.24963/ijcai.2023/140","url":"https://ijcai.org/proceedings/2023/140","pdf":"https://ijcai.org/proceedings/2023/0140.pdf","markdown":"https://ijcai.org/proceedings/2023/140.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/140","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":140,"pages":{"first":1258,"last":1266},"keywords":["Multidisciplinary Topics and Applications: MDA: Arts and creativity","Computer Vision: CV: 3D computer vision","Computer Vision: CV: Neural generative models, auto encoders, GANs"],"videos":[],"references":[{"position":1,"raw":"[Chan et al., 2021] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d- aware image synthesis. 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