ChildlikeSHAPES：面向动画人物画的语义层次区域解析
ChildlikeSHAPES: Semantic Hierarchical Region Parsing for Animating Figure Drawings
AI总结：
本文提出基于预训练SAM的层次分割模型ChildlikeSHAPES，用于解析儿童风格人物画的语义区域，在精度上超越现有模型，并构建了含16000幅儿童画、25类像素级标注的数据集，支撑自动面部动画、重新光照等应用。
摘要：
儿童风格人物画是人类最容易接触到的角色表达形式之一，但自动分析其内容仍然是一项重大挑战。尽管真实人物的语义分割近年来取得了显著进展，现有模型在面对儿童画的抽象性和表征性时往往失效。这种语义理解是动画工具的关键前提，这些工具希望在保留独特风格的同时修改人物。为帮助实现这一目标，我们提出了一种新颖的层次分割模型，该模型建立在架构和预训练SAM之上，以快速准确地获取这些语义标签。我们的模型在精度上超过了专注于真实人物和卡通人物的最先进分割模型，即使在微调之后也是如此。我们通过多个应用展示了该模型在语义分割方面的价值：一个全自动面部动画流水线、一个人物重新光照流水线、对现有儿童风格人物画动画方法的改进，以及对域外人物的泛化。最后，为支持该领域的未来工作，我们引入了一个包含16,000幅儿童画的数据集，该数据集具有25个语义类别的像素级标注。我们的工作可以催生全新的、易于使用的手绘角色动画工具，并且我们的数据集可以在各种图形学和以人为中心的研究领域开辟新的研究方向。
英文摘要：
Childlike human figure drawings represent one of humanity's most accessible forms of character expression, yet automatically analyzing their contents remains a significant challenge. While semantic segmentation of realistic humans has recently advanced considerably, existing models often fail when confronted with the abstract, representational nature of childlike drawings. This semantic understanding is a crucial prerequisite for animation tools that seek to modify figures while preserving their unique style. To help achieve this, we propose a novel hierarchical segmentation model, built upon the architecture and pre-trained SAM, to quickly and accurately obtain these semantic labels. Our model achieves higher accuracy than state-of-the-art segmentation models focused on realistic humans and cartoon figures, even after fine-tuning. We demonstrate the value of our model for semantic segmentation through multiple applications: a fully automatic facial animation pipeline, a figure relighting pipeline, improvements to an existing childlike human figure drawing animation method, and generalization to out-of-domain figures. Finally, to support future work in this area, we introduce a dataset of 16,000 childlike drawings with pixel-level annotations across 25 semantic categories. Our work can enable entirely new, easily accessible tools for hand-drawn character animation, and our dataset can enable new lines of inquiry in a variety of graphics and human-centric research fields.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
