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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.17168 |
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Table of Contents:
- Gaze and head movements play a central role in expressive 3D media, human-agent interaction, and immersive communication. Existing works often model facial components in isolation and lack mechanisms for generating personalized, style-aware gaze behaviors. We propose StyGazeTalk, a multimodal framework that synthesizes synchronized gaze-head dynamics with controllable styles. To support high-fidelity training, we construct HAGE, a high-precision multimodal dataset containing eye-tracking data, audio, head pose, and 3D facial parameters. Experiments show that our method produces temporally coherent, style-consistent gaze-head motions, enhancing realism in 3D face generation.