用关键点建模人体光影，生成更真实精准的阴影。
KPLM-STA: Physically-Accurate Shadow Synthesis for Human Relighting via Keypoint-Based Light Modeling
- 基于9个关键点和边界框建模人体，实现动态关节光照。
- 通过几何算法精确计算阴影角度、长度与位置，提升准确性。
- 在复杂姿态和多方向光照下表现优异，适合图像合成场景。
图像合成旨在无缝融合前景物体与背景，其中生成逼真且几何准确的阴影仍是持续挑战。尽管近期基于扩散模型的方法已超越基于GAN的方法，但现有技术如基于扩散的重布光框架IC-Light，在复合图像中仍难以同时实现高外观真实感与几何精度。为此，我们提出一种基于关键点线性模型（KPLM）与阴影三角算法（STA）的新阴影生成框架。KPLM利用9个关键点和1个边界框建模可动人体，实现关节处动态光照与物理合理阴影投射，提升视觉真实感；STA通过显式几何公式计算阴影角度、长度与空间位置，进一步提高几何精度。大量实验表明，该方法在阴影真实感基准上达到当前最优性能，尤其在复杂人体姿态下表现突出，并能有效泛化至多方向重布光场景，如IC-Light支持的情形。
原文摘要 · Abstract (English)
Image composition aims to seamlessly integrate a foreground object into a background, where generating realistic and geometrically accurate shadows remains a persistent challenge. While recent diffusion-based methods have outperformed GAN-based approaches, existing techniques, such as the diffusion-based relighting framework IC-Light, still fall short in producing shadows with both high appearance realism and geometric precision, especially in composite images. To address these limitations, we propose a novel shadow generation framework based on a Keypoints Linear Model (KPLM) and a Shadow Triangle Algorithm (STA). KPLM models articulated human bodies using nine keypoints and one bounding block, enabling physically plausible shadow projection and dynamic shading across joints, thereby enhancing visual realism. STA further improves geometric accuracy by computing shadow angles, lengths, and spatial positions through explicit geometric formulations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on shadow realism benchmarks, particularly under complex human poses, and generalizes effectively to multi-directional relighting scenarios such as those supported by IC-Light.
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