Clustered vector textures

epetitive vector patterns are common in a variety of applications but can be challenging and tedious to create. Existing automatic synthesis methods target relatively simple, unstructured patterns such as discrete elements and continuous Bézier curves. This paper proposes an algorithm for generating vector patterns with diverse shapes and structured local interactions via a sample-based representation. Our main idea is adding explicit clustering as part of neighborhood similarity and iterative sample optimization for more robust sample synthesis and pattern reconstruction. The results indicate that our method can outperform existing methods on synthesizing a variety of structured vector textures. Our project page is available at https://phtu-cs.github.io/cvt-sig22/.