Weakness]
- This paper is poorly written.
- The motivation, the contribution, and the intuition of this work can barely be understood based on the introduction.
- Sharing the style of citations and bullet items is confusing.
- Representing a translation vector with the notation $t^{i} = [x^{i}, y^{i}, z^{i} ] ^{T}$ is usually more preferred.
- The experimental results are not convincing.
- The descriptions of baseline models are unclear.
- Comparing the performance of the methods which directly optimize the mesh parameters, rotation, translation, and focal length according to the metric provided (projection error) doesn't make sense since the objective is in a different domain of the measurement of the performance.
- Comparing the performance of the model only pre-trained on synthetic data is unfair; instead, demonstrating that the proposed three projection errors are important is more preferred. In other words, providing the performance of the models pre-trained on synthetic data but fine-tuned on real-world datasets with different losses is necessary.
- The reason of claiming that it is a supervised learning framework is unclear. In my opinion, the supervision signals are still labeled. [Reproducibility]
The proposed framework is very simple and well explained with sufficient description of network parameters and optimization details. I believe it's trivial to reproduce the results. [Overall]
In term of the proposed framework, this paper only shows the improvement gained of fine-tuning the model based on the proposed losses defined by the reprojection errors of key points, optical flow, and foreground-background segmentation.
Taking into account that this work does show that fine-tuning the model pre-trained on synthetic datasets on real-world video clips improves the performance especially, it's still a convicting article.
In sum, as far as I am concerned this work makes a contribution but is insufficient.
- Comparing the performance of the model only pre-trained on synthetic data is unfair; instead, demonstrating that the proposed three projection errors are important is more preferred. In other words, providing the performance of the models pre-trained on synthetic data but fine-tuned on real-world datasets with different losses is necessary.