RealTalk: Realistic Emotion-Aware Lifelike Talking-Head Synthesis

• URL: http://arxiv.org/abs/2508.12163v1
• Date: Sat, 16 Aug 2025 21:28:22 GMT
• Title: RealTalk: Realistic Emotion-Aware Lifelike Talking-Head Synthesis
• Authors: Wenqing Wang, Yun Fu,
• Abstract summary: We introduce RealTalk, a framework for emotional talking heads with high emotion accuracy, enhanced emotion controllability, and robust identity preservation.<n>RealTalk employs a variational autoencoder to generate 3D facial landmarks from driving audio, which are synthesizing with emotion-label embeddings.<n>Experiments demonstrate that RealTalk outperforms existing methods in emotion accuracy, controllability, and identity preservation.
• Score: 47.6666060652434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion is a critical component of artificial social intelligence. However, while current methods excel in lip synchronization and image quality, they often fail to generate accurate and controllable emotional expressions while preserving the subject's identity. To address this challenge, we introduce RealTalk, a novel framework for synthesizing emotional talking heads with high emotion accuracy, enhanced emotion controllability, and robust identity preservation. RealTalk employs a variational autoencoder (VAE) to generate 3D facial landmarks from driving audio, which are concatenated with emotion-label embeddings using a ResNet-based landmark deformation model (LDM) to produce emotional landmarks. These landmarks and facial blendshape coefficients jointly condition a novel tri-plane attention Neural Radiance Field (NeRF) to synthesize highly realistic emotional talking heads. Extensive experiments demonstrate that RealTalk outperforms existing methods in emotion accuracy, controllability, and identity preservation, advancing the development of socially intelligent AI systems.

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