EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis

• URL: http://arxiv.org/abs/2502.00654v1
• Date: Sun, 02 Feb 2025 04:01:54 GMT
• Title: EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis
• Authors: Junuk Cha, Seongro Yoon, Valeriya Strizhkova, Francois Bremond, Seungryul Baek,
• Abstract summary: 3D Gaussian splatting-based talking head has recently gained attention for its ability to render high-fidelity images with real-time inference speed. We propose a lip-aligned emotional face generator and leverage it to train our EmoGaussian model. We experiment our EmoGaussian on publicly available videos and have obtained better results than state-of-the-arts in terms of image quality.
• Score: 4.895009594051343
• License:
• Abstract: 3D Gaussian splatting-based talking head synthesis has recently gained attention for its ability to render high-fidelity images with real-time inference speed. However, since it is typically trained on only a short video that lacks the diversity in facial emotions, the resultant talking heads struggle to represent a wide range of emotions. To address this issue, we propose a lip-aligned emotional face generator and leverage it to train our EmoTalkingGaussian model. It is able to manipulate facial emotions conditioned on continuous emotion values (i.e., valence and arousal); while retaining synchronization of lip movements with input audio. Additionally, to achieve the accurate lip synchronization for in-the-wild audio, we introduce a self-supervised learning method that leverages a text-to-speech network and a visual-audio synchronization network. We experiment our EmoTalkingGaussian on publicly available videos and have obtained better results than state-of-the-arts in terms of image quality (measured in PSNR, SSIM, LPIPS), emotion expression (measured in V-RMSE, A-RMSE, V-SA, A-SA, Emotion Accuracy), and lip synchronization (measured in LMD, Sync-E, Sync-C), respectively.

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