解决头像与身体融合的边界瑕疵问题，提升工业级数字内容制作质量。
Towards High-fidelity Head Blending with Chroma Keying for Industrial Applications
- 分步处理背景与前景，避免融合干扰
- 用色键生成无瑕疵背景，结合头部与长发增强数据
- 注意力机制聚焦关键区域，实现高保真融合
我们提出一种面向工业应用的头部融合流水线CHANGER，用于在数字内容创作中无缝将演员头部合成到目标身体上。核心挑战在于头部形状和发型差异导致的不自然边界与融合伪影。现有方法将前景与背景处理视为单一任务，影响融合质量。为此，我们提出将背景整合与前景融合解耦的新流程：利用色键技术生成无伪影背景，并引入头型与长发增强（$H^2$增强）以模拟多样化的头部形态与发型，显著提升对真实世界复杂情况的泛化能力。此外，提出的前景预测注意力变换器（FPAT）模块能预测并聚焦关键头身区域，优化融合效果。在基准数据集上的定量与定性评估表明，CHANGER优于当前最先进方法，可生成高质量、工业级的融合结果。
原文摘要 · Abstract (English)
We introduce an industrial Head Blending pipeline for the task of seamlessly integrating an actor's head onto a target body in digital content creation. The key challenge stems from discrepancies in head shape and hair structure, which lead to unnatural boundaries and blending artifacts. Existing methods treat foreground and background as a single task, resulting in suboptimal blending quality. To address this problem, we propose CHANGER, a novel pipeline that decouples background integration from foreground blending. By utilizing chroma keying for artifact-free background generation and introducing Head shape and long Hair augmentation ($H^2$ augmentation) to simulate a wide range of head shapes and hair styles, CHANGER improves generalization on innumerable various real-world cases. Furthermore, our Foreground Predictive Attention Transformer (FPAT) module enhances foreground blending by predicting and focusing on key head and body regions. Quantitative and qualitative evaluations on benchmark datasets demonstrate that our CHANGER outperforms state-of-the-art methods, delivering high-fidelity, industrial-grade results.
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