reviews: 
Comments: This work describes a method for single-shot 3D reconstruction and animation of a given 2D facial image. It uses an image to plane method to lift a 2D face into the tri-plane of a 3D GAN. It then morphs the triplane by adding a delta tri-plane which encoded the target expression via an PNCC encode image to animate the face. Differently from prior work the proposed method provides a solution for both video and audio driven animation of the face and also provides a super-resolution module for correctly morphing the torso and in-filling the exposed background regions. The proposed method is compared to several existing baseline video and audio-driven methods and shown to be superior them.
Strengths: The paper addresses a fairly novel problem of single-shot joint 3D reconstruction and animation of facial images. Differently from prior work it seeks to inject the animation information directly into the 3D representation versus the dominant approach of animating in 2D first and then lifting into 3D. As the authors correctly point out animation in 3D versus 2D results is more correct handling of large head poses and less warping artifacts. So this is an important problem to address towards enabling large head pose facial talking head generation. 

In comparison to the existing works on the topic, this work introduces a joint framework for both video and audio driven animation of 3D facial representations by encoding the audio signal into a PNCC representation. It also does a nice job of proposing to handle the torso and the background as a part of the overall solution thus enabling a more production-ready complete end-to-end framework. This is an often neglected detail in many works that treat the head in isolation from the backgrounds in which it exists requiring additional pre and post processing steps to deal with the torso and background.
Weaknesses: 1. The method by design requires the canonicalization, i.e., removal of the source image's facial expression, for the driving expression to be successfully applied to it. This is because the PNCC code is derived purely from the target driving video/audio's expression and hence cannot contain the information to erase/neutralize the source image's facial expression. I think the proposed method achieves this canonicalization during the fine-tuning phase with the Celeb-V-HQ video dataset. However, in my experience without any further stronger constraints, the proposed method cannot fully remove the source image's original facial expression. Can the author show examples of these canonicalized 3D face reconstructions of the input images without applying the target expression? Related to this is the question of how robust is the proposed method to the presence of large facial expressions in the source image. Does it work for expressions where the source image has a wide open mouth and lowered jaw or nearly closed eyes, for example?

2. Overall the proposed methods is an obvious combination of several existing ideas from prior works on the topic to culminate in a successful large engineered end-to-end solution. For example, the idea of using I2P was previously proposed in LP3D; the idea of using rendered target expression images from a 3DMM was proposed in Li et al., 2023b, the audio to PNCC code predictor is borrowed from prior work; and the torso warping module is also borrowed from Wang et al., 2021. While the proposed combination of existing ideas results in an effective solution, from the research perspective the overall solution is light on significant novel or surprising insights.

3. The authors don't specify the dataset/protocol that they used for evaluation.

4. Comparisons of the proposed method to several of the newer (than Face-Vid2Vid) and better performing video-driven 2D facial animation methods are missing. Below I list several of them.

[1] Thin-plate spline motion model for image animation., CVPR 2022

[2] Depth-Aware Generative Adversarial Network for Talking Head Video Generation., CVPR 2022

[3] FNeVR: Neural volume rendering for face animation., NeurIPS 2022

[4] Latent Image Animator: Learning to Animate Images via Latent Space Navigation., ICLR 2022

[5] DPE: Disentanglement of Pose and Expression for General Video Portrait Editing., CVPR 2023

[6] Conditioned Memory Compensation Network for Talking Head video Generation., ICCV 2023
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent