Weakness:
Lack of novelty. The key idea, i.e., combining foreground masks to remove the artifacts from the background, is not new. Separate handling of foreground from background is a common practice for dynamic scene novel view synthesis, and many recent methods do not even require the foreground masks for modeling dynamic scenes (they jointly model the foreground region prediction module, e.g., Tretschk et al. 2021).
Lack of controllability. 1) The proposed method cannot handle the headpose. While this paper defers this problem to a future work, a previous work (e.g., Gafni et al. ICCV 2021) is already able to control both facial expression and headpose. Why is it not possible to condition the headpose parameters in the NeRF beyond the facial expression similar to [Gafni et al. ICCV 2021]? 2) Even for controlling facial expression, it is highly limited to the mouth region only. From overall qualitative results and demo video, it is not clear the method indeed can handle overall facial expression including eyes, nose, and wrinkle details, and the diversity in mouth shape that the model can deliver is significantly limited.
Low quality. The results made by the proposed method are of quite low quality. 1) Low resolution: While many previous works introduce high-quality view synthesis results with high-resolution (512x512 or more), this paper shows low resolution results (256x256) for some reasons. Simply saying the problem of resources is not a convincing argument since many existing works already proved the feasibility of high resolution image synthesis using implicit function. Due to this low-resolution nature, many high-frequency details (e.g., facial wrinkles), which are the key to enabling photorealistic face image synthesis, are washed out. 2) In many cases, the conditioning facial expressions do not match that from the synthesized image. From the demo video, while mouth opening or closing are somehow synchronized with conditioning videos, there exists a mismatch in the style of the detailed mouth shape.
1) The proposed method cannot handle the headpose. While this paper defers this problem to a future work, a previous work (e.g., Gafni et al. ICCV 2021) is already able to control both facial expression and headpose. Why is it not possible to condition the headpose parameters in the NeRF beyond the facial expression similar to [Gafni et al. ICCV 2021]?