Collaborative Neural Rendering using Anime Character Sheets
This addresses the problem of reducing manual effort for artists in anime production, though it appears incremental as it builds on existing neural rendering techniques for a specific domain.
The paper tackles the laborious task of generating anime character images in desired poses by introducing the Collaborative Neural Rendering (CoNR) method, which uses a few reference images and a landmark encoding to avoid universal body models, achieving improved performance with multiple references and releasing a dataset of over 700,000 images.
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.