一键将动漫图转为可动2.5D模型，自动分层并补全被遮挡部分
See-through: Single-image Layer Decomposition for Anime Characters
- 通过扩散模型与伪深度推理，从单图分解出带绘制顺序的语义层
- 利用商业Live2D模型生成高质量监督数据，解决训练样本少问题
- 支持动态分层重建，适合专业实时动画制作
我们提出一个框架，可将静态动漫插画自动化转换为可操控的2.5D模型。现有专业流程需手动分割并人工补全被遮挡区域以实现动作，耗时且依赖主观创作。本方法通过单图分解出完全补全、语义清晰且推断出绘制顺序的多层图像，克服该瓶颈。为应对训练数据稀缺，我们构建可扩展引擎，从商业Live2D模型中提取像素级语义与隐藏几何信息，生成高质量监督信号。方法结合基于扩散的肢体一致性模块（保证全局几何一致）与像素级伪深度推理机制，有效处理动漫角色复杂的层次结构，如交错的发丝。实验表明，该方法生成的模型具有高保真度且可实时操控，适用于专业动画应用。
原文摘要 · Abstract (English)
We introduce a framework that automates the transformation of static anime illustrations into manipulatable 2.5D models. Current professional workflows require tedious manual segmentation and the artistic ``hallucination'' of occluded regions to enable motion. Our approach overcomes this by decomposing a single image into fully inpainted, semantically distinct layers with inferred drawing orders. To address the scarcity of training data, we introduce a scalable engine that bootstraps high-quality supervision from commercial Live2D models, capturing pixel-perfect semantics and hidden geometry. Our methodology couples a diffusion-based Body Part Consistency Module, which enforces global geometric coherence, with a pixel-level pseudo-depth inference mechanism. This combination resolves the intricate stratification of anime characters, e.g., interleaving hair strands, allowing for dynamic layer reconstruction. We demonstrate that our approach yields high-fidelity, manipulatable models suitable for professional, real-time animation applications.
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