This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and preserve the spatial distribution of semantic parts. Meanwhile, in order to improve the prediction for textures of invisible parts, we explicitly enforce the consistency across different views of the same subject by exchanging the textures predicted by two views to render images during training. The perception loss and total variation regularization are optimized to maximize the similarity between rendered and input images, which does not necessitate extra 3D texture supervision. Experimental results on pedestrian images and fashion photos demonstrate that our method can produce higher quality textures with convincing details than other texture generation methods.

Weaknesses: - It's unclear if there is significant improvement over RSTG[33] from Figure 5. In particular, the results are only compared from the frontal view, the approach should be compared with [33] that shows multiple views of the image. The results of [33] is not compared on DeepFashion. In fact, CMR looks a lot worse perceptually than RSTG in Figure 5(a), however there is a significant difference in mask-SSIM which is a bit peculiar. For human body shpaes, the simple spherical UV mapping introduces quite a significant distortion.

Following the author response and the online discussion, concerns were resolved and all reviewers recommend the paper for acceptance. The authors are asked to address the reviewers' comments in the final version.

This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and preserve the spatial distribution of semantic parts. Meanwhile, in order to improve the prediction for textures of invisible parts, we explicitly enforce the consistency across different views of the same subject by exchanging the textures predicted by two views to render images during training. The perception loss and total variation regularization are optimized to maximize the similarity between rendered and input images, which does not necessitate extra 3D texture supervision. Experimental results on pedestrian images and fashion photos demonstrate that our method can produce higher quality textures with convincing details than other texture generation methods.