{"type":"ScholarlyArticle","title":"Collaborative Neural Rendering Using Anime Character Sheets","authors":[{"name":"Zuzeng Lin","slug":"zuzeng-lin","given":"Zuzeng","family":"Lin","affiliation":"Tianjin University; Megvii Technology"},{"name":"Ailin Huang","slug":"ailin-huang","given":"Ailin","family":"Huang","affiliation":"Megvii Technology; Wuhan University"},{"name":"Zhewei Huang","slug":"zhewei-huang","given":"Zhewei","family":"Huang","affiliation":"Megvii Technology"}],"abstract":"Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). In general, the diverse hairstyles and garments of anime characters defies the employment of universal body models like SMPL, which fits in most nude human shapes. To overcome this, CoNR uses a compact and easy-to-obtain landmark encoding to avoid creating a unified UV mapping in the pipeline. In addition, the performance of CoNR can be significantly improved when referring to multiple reference images, thanks to feature space cross-view warping in a carefully designed neural network. Moreover, we have collected a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area. The code and dataset is available at https://github.com/megvii-research/IJCAI2023-CoNR.","doi":"10.24963/ijcai.2023/646","url":"https://ijcai.org/proceedings/2023/646","pdf":"https://ijcai.org/proceedings/2023/0646.pdf","markdown":"https://ijcai.org/proceedings/2023/646.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/646","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":"AI and Arts","track":"AI and Arts","number":646,"pages":{"first":5824,"last":5832},"keywords":["Application domains: Images and visual arts","Methods and resources: Datasets, knowledge bases and ontologies"],"videos":[],"references":[{"position":1,"raw":"[Alldieck et al., 2019] Thiemo Alldieck, Gerard Pons-Moll, Chris- tian Theobalt, and Marcus Magnor. 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