  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.
