While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent methods, e.g., adversarial texture optimization, better handle lower-quality data obtained from hand-held devices, we find them to still struggle frequently. To improve robustness, particularly of recent adversarial texture optimization, we develop an explicit initialization and an alignment procedure. It deals with complex geometry due to a robust mapping of the geometry to the texture map and a hard-assignment-based initialization. It deals with misalignment of geometry and images by integrating fast image-alignment into the texture refinement optimization. We demonstrate efficacy of our texture generation on a dataset of 11 scenes with a total of 2807 frames, observing 7.8% and 11.1% relative improvements regarding perceptual and sharpness measurements.

翻译：虽然从图像和视频数据中恢复几何学在计算机视觉中受到了很多注意,但为某一几何采集纹理的方法并不那么成熟。 具体地说, 典型的纹理生成方法往往采用干净的几何和合理一致的图像数据。 虽然最近的方法,例如对抗性纹理优化,更好地处理手持装置获得的低质量数据,但我们发现它们仍然经常在挣扎。 为提高稳健性,特别是最近的对抗性纹理优化,我们开发了一个明确的初始化和校正程序。 它处理复杂的几何学,因为对纹理图的几何图绘制得力强和硬性定型初始化。 它处理几何和图像的不匹配问题,将快速图像对齐纳入纹理优化。 我们用总共2807个框架的11个数据集展示了我们的纹理生成的功效,对感知性和锐度测量的7.8%和11.1%的相对改进率。