论文标题
基于自动模板的人类模型匹配的高分辨率增强
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
论文作者
论文摘要
我们提出了一种新的方法，用于3D形状的可变形人形状匹配。我们的方法基于共同采用三种不同的工具：固有的光谱匹配管道，可变形模型和外部细节的改进。通过结合使用，这些工具使我们能够大大提高匹配的质量，同时解决每个工具分别显示的关键问题。在本文中，我们提出了一种创新的高分辨率增强（HRA）策略，即使在输入形状之间存在明显的网格分辨率不匹配的情况下，也能够高度准确地对应。这种增强为采用的形态模型施加的解决方案限制提供了有效的解决方法。 HRA在其全局和局部版本中代表了表面细分方法的新颖精致策略。我们证明了提议的管道对多个具有挑战性的基准测试的准确性，并展示了其在表面注册和纹理转移中的有效性。
We propose a new approach for 3D shape matching of deformable human shapes. Our approach is based on the joint adoption of three different tools: an intrinsic spectral matching pipeline, a morphable model, and an extrinsic details refinement. By operating in conjunction, these tools allow us to greatly improve the quality of the matching while at the same time resolving the key issues exhibited by each tool individually. In this paper we present an innovative High-Resolution Augmentation (HRA) strategy that enables highly accurate correspondence even in the presence of significant mesh resolution mismatch between the input shapes. This augmentation provides an effective workaround for the resolution limitations imposed by the adopted morphable model. The HRA in its global and localized versions represents a novel refinement strategy for surface subdivision methods. We demonstrate the accuracy of the proposed pipeline on multiple challenging benchmarks, and showcase its effectiveness in surface registration and texture transfer.
