论文标题
使用动漫角色表的协作神经渲染
Collaborative Neural Rendering using Anime Character Sheets
论文作者
论文摘要
绘制带有所需姿势的人物的图像是动漫制作中必不可少但费力的任务。近年来，协助艺术家创建的是研究热点。在本文中，我们介绍了协作神经渲染方法（CONR）方法，该方法为指定的姿势创建了来自一些参考图像（又称字符表）的新图像。通常，动漫角色的多样化发型和服装违反了诸如SMPL之类的通用身体模型的利用，SMPL适合大多数裸体的人类形状。为了克服这一点，Conr使用紧凑且易于访问的地标编码，以避免在管道中创建统一的UV映射。此外，由于在精心设计的神经网络中的特征空间跨视图，在参考多个参考图像时，CONR的性能可以显着提高。此外，我们收集了一个字符表数据集，该数据集包含超过700,000个手绘和合成的不同姿势图像，以促进该领域的研究。我们的代码和演示可在https://github.com/megvii-research/ijcai2023-conr上找到。
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.
