- In L104-112 several prior arts are listed. I understand that the task authors tackle is predicting full mesh, but why proposed method is better than [21] or [6]? What makes the proposed approach better than previous methods? From the experiments, the performance difference is clear. However, I am missing the core insights/motivations behind the approach. - In L230, it is indicated that "we allow it (3D pose regressor) to output M possible predictions and, out of this set, we select the one that minimizes the MPJPE/RE metric". Comparison here seems a bit unfair. Instead of using oracle poses, the authors would compute the MPJPE/RE for all of the M or maybe n out of M poses, then report the median error. - It is not clearly indicated whether the curated AH36M dataset is used for training. If so, did other methods eg. HMR, SPIN have access to AH36M data during training for a fair comparison? - There is no promise to release the code and the data. Even though the method is explained clearly, a standard implementation would be quite helpful for the research community. - There is no failure cases/limitations sections. It would be insightful to include such information for researchers who would like to build on this work.
- It is not clearly indicated whether the curated AH36M dataset is used for training. If so, did other methods eg. HMR, SPIN have access to AH36M data during training for a fair comparison?