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. Code is available at this https URL

通过使用全新的前向多视图和多阶段合成过程以及语义地图引导策略，提出了一种快速而通用的交互式纹理转移的神经网络框架，从而在粗到细的方式下合成高质量的结果，同时保留了结构和纹理的细节和连贯性，并且相较于交互式纹理转移算法，不仅能够获得更高质量的结果，而且速度也提高了 2-5 个数量级。