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.

翻译：绘制具有指定姿态的角色图像是动漫制作中一项重要但费力的任务。近年来，协助创作者进行绘制已成为研究热点。本文提出协同神经渲染（CoNR）方法，该方法能从少量参考图像（即角色设定表）为指定姿态生成新图像。总体而言，动漫角色多样化的发型与服饰使得通用人体模型（如适用于多数裸体人形的SMPL）难以适用。为克服此问题，CoNR采用紧凑且易于获取的地标编码，避免在流程中创建统一UV映射。此外，通过精心设计的神经网络的跨视角特征空间扭曲，CoNR在参考多张参考图像时性能可显著提升。我们还收集了一个包含超过70万张手绘及合成图像的角色设定表数据集，涵盖多种姿态，以推动该领域研究。我们的代码与演示可在 https://github.com/megvii-research/IJCAI2023-CoNR 获取。