让单视角穿衣人体模型侧视更真实，解决全局拓扑与局部表面不一致问题。
CHRIS: Clothed Human Reconstruction with Side View Consistency
- 引入侧视法向判别器，提升整体视觉合理性。
- 设计多点到一点梯度计算，确保局部表面平滑一致。
- 适合影视制作与混合现实中的高保真人像重建场景。
从单张RGB图像生成逼真的穿衣人体模型，在混合现实和影视制作中至关重要。尽管近年来取得进展，主流方法通常无法充分利用侧视信息，因为输入仅含正面视图，导致侧视图出现全局拓扑失真和局部表面不一致。为此，本文提出衣着人体侧视一致性重建方法CHRIS，包含：1）侧视法向判别器，通过区分生成的侧视法向与真实法向，增强全局视觉合理性；2）多对一梯度计算（M2O），通过整合邻近点的梯度来计算采样点梯度，实现局部表面平滑。实验表明，CHRIS在公开基准上达到领先性能，优于先前工作。
原文摘要 · Abstract (English)
Creating a realistic clothed human from a single-view RGB image is crucial for applications like mixed reality and filmmaking. Despite some progress in recent years, mainstream methods often fail to fully utilize side-view information, as the input single-view image contains front-view information only. This leads to globally unrealistic topology and local surface inconsistency in side views. To address these, we introduce Clothed Human Reconstruction with Side View Consistency, namely CHRIS, which consists of 1) A Side-View Normal Discriminator that enhances global visual reasonability by distinguishing the generated side-view normals from the ground truth ones; 2) A Multi-to-One Gradient Computation (M2O) that ensures local surface consistency. M2O calculates the gradient of a sampling point by integrating the gradients of the nearby points, effectively acting as a smooth operation. Experimental results demonstrate that CHRIS achieves state-of-the-art performance on public benchmarks and outperforms the prior work.
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