Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: In this paper, the authors study the problem of miscalibration in human pose lifting when utilising the Mean Per-Joint Prediction Error as a loss function. The authors provide both simluations using a simple baseline and experiments using well-known human pose estimation models, showing the issue of miscalibration in practice.
strengths_and_weaknesses: **Strengths**

- The paper is well-written and easy to follow
- The authors have provided both a baseline and results on well-known models in HPE to validate their hypothesis
- The paper demonstrates that it is worth looking at lifting models from a probabilistic perspective, especially when considering multiple-hypothesis models. This opens up new possibilities for the study of HPE, which often is thought of as a purely geometrical problem.
- The proposed finding easily opens up models that maximize the likelihood of the data explicitly, such as VAEs, Flows, or Diffusion models. I believe that the findings in this paper can be a great starting point for people looking to implement probabilistic solutions for HPE

**Weaknesses**

- The claim of miscalibration is definitely important, but in the end, HPE is a geometric problem whose solution is measured as the distance of predicted and ground-truth joints in 3D space. That being the case, MPJPE provides a good measure of the "accuracy" of the estimation. Perhaps the real problem is the fact that this measure is the target to be optimized and that numerous papers will directly optimize it to maximize performance on different benchmarks. Nevertheless, having a correct mean prediction is a very important aspect, something which as seen in Table 1 (Gaussian MPJPE vs NLL), simple NLL-based models fail to do as well. I believe the authors could have provided a couple of sentences regarding this aspect
suggested_changes: I believe that if the authors could address the only weakness listed in the review, which already contains some suggestions, the paper would definitely be more complete.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The authors aim to demonstrate that the standard approach for pose lifting (from 2D to 3D), by minimization of the MPJPE loss, leads to overconfident models, that do not represent correctly the uncertainty of this ill-posed problem. Through experiments and formal demonstrations, the paper shows that minMPJPE generates lower-variance models (overconfident), when compared to a model trained with Negative Log Likelyhood, even though the mean error is lower (better).
strengths_and_weaknesses: ### Strengths
1. The paper is clearly written and easy to follow.
1. The experiments support the hypothesis presented.
1. The figure is clear and well designed.
1. The work seems to be reproducible using the information in the main paper.
### Weaknesses
1. In fig.1, $x^*$ is not explained. By context I assume it's the Ground Truth 3D pose, but a clarification is needed.
suggested_changes: 1. See weaknesses
review_title: null

Decision: Invite to present (notable)