Deep Exemplar-based Color Transfer for 3D Model

Recoloring 3D models is a challenging task that often requires professional knowledge and tedious manual efforts. In this paper, we present the first deep-learning framework for exemplar-based 3D model recolor, which can automatically transfer the colors from a reference image to the 3D model texture. Our framework consists of two modules to solve two major challenges in the 3D color transfer. First, we propose a new feed-forward Color Transfer Network to achieve high-quality semantic-level color transfer by finding dense semantic correspondences between images. Second, considering 3D model constraints such as UV mapping, we design a novel 3D Texture Optimization Module which can generate a seamless and coherent texture by combining color transferred results rendered in multiple views. Experiments show that our method performs robustly and generalizes well to various kinds of models.