TY - JOUR AU - Wang, Zhizhong AU - Zhao, Lei AU - Chen, Haibo AU - Li, Ailin AU - Zuo, Zhiwen AU - Xing, Wei AU - Lu, Dongming PY - 2022/06/28 Y2 - 2024/03/28 TI - Texture Reformer: Towards Fast and Universal Interactive Texture Transfer JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 3 SE - AAAI Technical Track on Computer Vision III DO - 10.1609/aaai.v36i3.20164 UR - https://ojs.aaai.org/index.php/AAAI/article/view/20164 SP - 2624-2632 AB - In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2) the simplicity of guidance maps, and 3) the execution efficiency. To address these challenges, our key idea is to use a novel feed-forward multi-view and multi-stage synthesis procedure consisting of I) a global view structure alignment stage, II) a local view texture refinement stage, and III) a holistic effect enhancement stage to synthesize high-quality results with coherent structures and fine texture details in a coarse-to-fine fashion. In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer. The experimental results on a variety of application scenarios demonstrate the effectiveness and superiority of our framework. And compared with the state-of-the-art interactive texture transfer algorithms, it not only achieves higher quality results but, more remarkably, also is 2-5 orders of magnitude faster. ER -