{"ID":2868204,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17168","arxiv_id":"2509.17168","title":"StyGazeTalk: Learning Stylized Generation of Gaze and Head Dynamics","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.17168","url_pdf":"https://arxiv.org/pdf/2509.17168v2","authors":"[\"Chengwei Shi\",\"Chong Cao\"]","published":"2025-09-21T17:27:57Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.CV\"]","methods":"[]","has_code":false}