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Neural radiance fields (NeRF) based methods have shown amazing performance insynthesizing 3D-consistent photographic images, but fail to generate multi-viewportrait drawings. The key is that the basic assumption of these methods -- asurface point is consistent when rendered from different views -- doesn't holdfor drawings. In a portrait drawing, the appearance of a facial point maychanges when viewed from different angles. Besides, portrait drawings usuallypresent little 3D information and suffer from insufficient training data. Tocombat this challenge, in this paper, we propose a Semantic-Aware GEnerator(SAGE) for synthesizing multi-view portrait drawings. Our motivation is thatfacial semantic labels are view-consistent and correlate with drawingtechniques. We therefore propose to collaboratively synthesize multi-viewsemantic maps and the corresponding portrait drawings. To facilitate training,we design a semantic-aware domain translator, which generates portrait drawingsbased on features of photographic faces. In addition, use data augmentation viasynthesis to mitigate collapsed results. We apply SAGE to synthesize multi-viewportrait drawings in diverse artistic styles. Experimental results show thatSAGE achieves significantly superior or highly competitive performance,compared to existing 3D-aware image synthesis methods. The codes are availableat https://github.com/AiArt-HDU/SAGE.

Semantic-aware Generation of Multi-view Portrait Drawings