Dr.3D: Adapting 3D GANs to Artistic Drawings

Autor:	Jin, Wonjoon, Ryu, Nuri, Kim, Geonung, Baek, Seung-Hwan, Cho, Sunghyun
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the inevitable geometric ambiguity present in drawings. To tackle this, we present Dr.3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings. Dr.3D is equipped with three novel components to handle the geometric ambiguity: a deformation-aware 3D synthesis network, an alternating adaptation of pose estimation and image synthesis, and geometric priors. Experiments show that our approach can successfully adapt 3D GANs to drawings and enable multi-view consistent semantic editing of drawings. Comment: Accepted to SIGGRAPH Asia 2022 (Conference Track). For project page, see https://jinwonjoon.github.io/dr3d/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.16798 Zobrazit plný text záznamu View this record from Arxiv