Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper presents a new method that creates full-body avatars from self-portrait monocular videos. The proposed method specifically models the body, the head, and the hands, the neural volumetric renderings of which are later fused together. Qualitative and quantitative evaluations demonstrate the superiority of the proposed method.
strengths_and_weaknesses: Paper strengths:
- The proposed new method demonstrates clear progress of neural human avatars, achieving sharp and clear renderings under novel views and novel poses.
- The proposed method is compared against strong existing baselines and ablated carefully.

Paper weaknesses:
- Despite the FLAME model being used for the head, its potential does not seem to be fully utilized. The facial expression remains static in the final results which significantly increases the uncanniness.
- The method description could benefit from more additional details. For instance, the latent code is mentioned several times but there lacks a description of it in the overall diagram. The $L^{body}$ loss was not defined in the main text.
- The non-rigid ray transformation seems to make the texture static across view directions. Was this the reason why the results seem quite rigid? Also, how does the proposed method perform when compared with a traditional graphics pipeline such as deformation transfer?
questions: It would be great if the authors could address the above mentioned points in the weaknesses section.
limitations: There are no potential negative societal impact of this paper.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes to reconstruct the human body with detailed hands and face, leveraging separately recorded hand and face videos.
strengths_and_weaknesses: Strengths
1. The proposed pipeline is sound. Leveraging separate videos for body, face and hands at different scales makes sense; the fusion of geometry and appearance is well designed; the use of non-rigid ray transformation technique for free-view novel pose synthesis is reasonable.  
2. The experiment results show more visually appealing results.
3. An additional SynTotalHuman dataset is proposed.

Weaknesses
My major concern is related to the use of parametric models as the foundation for reconstruction that undermines the technical significance of the paper. The reconstructed hands are claimed to be more refined than prior works, given that SMPL+H is used. Even without any replacement estimation, the SMPL+H already gives good geometry. This is particularly questionable as the examples shown all have bare hands. 
questions: Please see the Weaknesses section.
limitations: No additional limitations or potential negative societal impact.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This work aims at reconstructing the full-body human from several monocular self-rotation videos including the face, hands, and body videos. Compared to the priors, this work improves the local details significantly with the acceptable cost of several extra part videos and more training time. The experiment results prove the effectiveness of the proposed framework and each introduced component.
strengths_and_weaknesses: Strengths

- The proposed multi-part networks can utilize information from different part videos to learn better local details. The ablation study in  Figure5(a) and Table3 show the effectiveness of multi-part networks. I believe part-based data and modeling is the right way to enhance the details for complex objects like humans.  

- To connect the observation space and the canonical space, this work first uses part-based deformation fields to transform the sample points from each observation space, and then performs the geometry and appearance composition to generate a unified model.
 Sufficient experiments and comparisons have been done to show the superiority of TotalSelfScan.

- Two new datasets are introduced for part-based model evaluation. I believe they can also benefit the computer vision/graphics community.

- Limitations and reasonable potential future solutions have been well discussed. I believe these can also benefit the following works. 

Weaknesses

- The non-rigid ray transformation seems a nice strategy to alleviate the unnatural color artifacts. I would expect more qualitative comparisons and analysis on this part.

questions: Overall, this work is easy to follow and useful for real human reconstruction applications. I am interested in the prior of pose-dependent deformations discussed in the limitation. I think it is the key limitation of TotalSelfScan when applied to animation. I would expect more discussion on this part.
limitations: The limitations and potential negative societal impact have been well described.
review_title: null

Meta-review: metareview: This paper was reviewed by three experts in the field. Based on the reviewers' feedback, the decision is to recommend the paper for acceptance to NeurIPS 2022. 

The reviewers did raise some valuable concerns that should be addressed in the final camera-ready version of the paper. For example, more discussion can be added on the key limitation of TotalSelfScan when applied to animation. The authors are encouraged to make the necessary changes to the best of their ability. We congratulate the authors on the acceptance of their paper!
confidence: Certain
Decision: Accept