reviews: 
Comments: This work introduces an innovative approach for creating photorealistic animated talking head portraits starting from a single image and guided by either an audio speech input or a reference facial video. The core technique involves constructing a 3D facial model from the input image and subsequently applying motion control based on the provided audio or video guidance. Beyond facial animation, the approach also addresses realistic generation of upper body dynamics and scene context. The 3D face modeling component leverages a large-scale image-to-plane architecture. The animation module incorporates a newly developed facial motion adapter. The paper delivers comprehensive experimental validation through both visual and quantitative metrics, comparative studies against existing methods, perceptual user evaluations, and thorough component ablation analysis, all demonstrating the method's competitive performance.
Strengths: + The proposed approach presents compelling technical contributions with a well-designed pipeline that demonstrates notable originality, particularly in how it integrates the large-scale image-to-plane foundation, the motion adapter component, and the Head-Torso-Background Super-Resolution framework, yielding impressively photorealistic outputs.

+ The manuscript offers a thorough experimental investigation that substantiates the method's strong performance. The evaluation encompasses comparisons with multiple recent state-of-the-art baselines (though additional methods could strengthen this further), incorporates both visual and quantitative metrics, includes user studies valuable for assessing perceptual quality, and provides clear ablation analyses demonstrating each pipeline component's contribution. The included supplementary video materials effectively showcase the visual fidelity and comparative advantages over preceding methods.
Weaknesses: - The paper lacks citations to several relevant foundational works in the field:

J. S. Chung, A. Jamaludin, and A. Zisserman, "You said that?" in BMVC, 2017.

Ye, Z., Xia, M., Yi, R., Zhang, J., Lai, Y.-K., Huang, X., et al. (2022). Audio-driven talking face video generation with dynamic convolution kernels. IEEE Transactions on Multimedia.

Given that the approach builds on projected normalized coordinate code (PNCC) representation, a particularly significant omission is:

Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Niessner, M., Pérez, P., Richardt, C., Zollhöfer, M. and Theobalt, C., 2018. Deep video portraits. ACM transactions on graphics (TOG), 37(4), pp.1-14.

Since the approach enables speech-conditioned animation, the following seminal contribution should have been referenced:

Kim, H., Elgharib, M., Zollhöfer, M., Seidel, H.P., Beeler, T., Richardt, C. and Theobalt, C., 2019. Neural style-preserving visual dubbing. ACM Transactions on Graphics (TOG), 38(6), pp.1-13.

- In the audio-driven talking face generation experimental section, the comparative analysis would benefit from the inclusion of additional contemporary approaches addressing this problem, such as:

Yi, R., Ye, Z., Zhang, J., Bao, H. and Liu, Y.J., 2020. Audio-driven talking face video generation with learning-based personalized head pose. arXiv preprint arXiv:2002.10137.

Yao, S., Zhong, R., Yan, Y., Zhai, G. and Yang, X., 2022. DFA-NeRF: Personalized talking head generation via disentangled face attributes neural rendering. arXiv preprint arXiv:2201.00791.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 3 good