{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/collaborative-neural-rendering-using-anime","title":"Collaborative Neural Rendering using Anime Character Sheets","arxiv_id":"2207.05378","date":"2022-07-12","proceeding":null,"authors":["Zuzeng Lin","Ailin Huang","Zhewei Huang"],"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. Our code and demo are available at https://github.com/megvii-research/IJCAI2023-CoNR.","url_abs":"https://arxiv.org/abs/2207.05378v5","url_pdf":"https://arxiv.org/pdf/2207.05378v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"collaborative-neural-rendering-using-anime","repo_url":"https://github.com/transpchan/Live3D","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"collaborative-neural-rendering-using-anime","repo_url":"https://github.com/megvii-research/conr","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"collaborative-neural-rendering-using-anime","repo_url":"https://github.com/megvii-research/ijcai2023-conr","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"collaborative-neural-rendering-using-anime","repo_url":"https://github.com/transpchan/Live3D-v2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-3d","task_name":"Image to 3D"},{"task_slug":"image-to-video","task_name":"Image to Video Generation"},{"task_slug":"neural-rendering","task_name":"Neural Rendering"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[],"datasets_introduced":[{"slug":"ultradensepose","name":"UltraDensePose","full_name":"UltraDensePose"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.05378","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}