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GaussianSpeech: Audio-Driven Gaussian Avatars
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We introduce GaussianSpeech, a novel approach that synthesizes high-fidelity animation sequences of photo-realistic, personalized 3D human head avatars from spoken audio. To capture the expressive, detailed nature of human heads, including skin furrowing and finer-scale facial movements, we propose to couple speech signal with 3D Gaussian splatting to create realistic, temporally coherent motion sequences. We propose a compact and efficient 3DGS-based avatar representation that generates expression-dependent color and leverages wrinkle- and perceptually-based losses to synthesize facial details, including wrinkles that occur with different expressions. To enable sequence modeling of 3D Gaussian splats with audio, we devise an audio-conditioned transformer model capable of extracting lip and expression features directly from audio input. Due to the absence of high-quality datasets of talking humans in correspondence with audio, we captured a new large-scale multi-view dataset of audio-visual sequences of talking humans with native English accents and diverse facial geometry. GaussianSpeech consistently achieves state-of-the-art performance with visually natural motion at real time rendering rates, while encompassing diverse facial expressions and styles.
Forward citations
Cited by 4 Pith papers
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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images
FFAvatar uses a Transformer-based 3D Gaussian model with alternating attention and sparse-to-dense learning to enable feed-forward, incremental reconstruction of animatable 4D head avatars from sparse portrait images.
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MoGaFace: Momentum-Guided and Texture-Aware Gaussian Avatars for Consistent Facial Geometry
MoGaFace improves 3D head avatar rendering by combining momentum-based expression correction with latent texture attention in Gaussian splatting, boosting novel-view quality under imperfect mesh tracking.
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Few-Shot Identity Adaptation for 3D Talking Heads via Global Gaussian Field
A shared global Gaussian field plus identity embeddings lets a 3D talking head model adapt to new speakers with a few seconds of footage while improving quality over prior per-identity models.
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GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation
GGTalker combines large-scale audio-to-expression and expression-to-texture priors with rapid per-identity fine-tuning to create high-quality 3D talking heads from a short video.
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