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In this paper, we present the decomposed triplane-hash neural radiance fields(DT-NeRF), a framework that significantly improves the photorealistic renderingof talking faces and achieves state-of-the-art results on key evaluationdatasets. Our architecture decomposes the facial region into two specializedtriplanes: one specialized for representing the mouth, and the other for thebroader facial features. We introduce audio features as residual terms andintegrate them as query vectors into our model through an audio-mouth-facetransformer. Additionally, our method leverages the capabilities of NeuralRadiance Fields (NeRF) to enrich the volumetric representation of the entireface through additive volumetric rendering techniques. Comprehensiveexperimental evaluations corroborate the effectiveness and superiority of ourproposed approach.

DT-NeRF: Decomposed Triplane-Hash Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis