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Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting — arXiv:2609.17422

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17422
License (as archived): CC0-1.0
Archived: 2026-10-15T18:32:28.563Z
SHA-256 of the archived text: ddf0027344d543cacc6e086c4c28d78744ed752cee589872a4d354bfeb618361
Stable URL of this page: https://metraipolis.com/documento/dc_1359bee2ecac

How to cite

Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting — arXiv:2609.17422 — arXiv (en), https://arxiv.org/abs/2609.17422 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1359bee2ecac (Metraipolis archive dc_1359bee2ecac, sha256 ddf0027344d543cacc6e086c4c28d78744ed752cee589872a4d354bfeb618361)

BibTeX

@misc{dc1359bee2ecac,
  title = {Talking Head Synthesis with Facial Landmark Guidance via 3D Gaussian Splatting — arXiv:2609.17422},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17422},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1359bee2ecac; sha256 ddf0027344d543cacc6e086c4c28d78744ed752cee589872a4d354bfeb618361},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17422. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Audio-driven digital human generation plays an important role in virtual communication, immersive interaction, and media production. With the development of Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), recent talking-head systems have obtained more faithful 3D facial geometry and appearance modeling. A remaining difficulty is that speech features mainly describe temporal acoustic patterns rather than explicit facial layouts. As a result, directly driving 3D facial deformation with audio may produce inaccurate mouth motion, weak expression details, and local artifacts. To address this issue, we propose a facial-keypoint-guided spatial enhancement module. The predicted landmarks provide structural cues for selecting and enriching spatial points around expression-sensitive facial regions. We further introduce a global landmark compensation mechanism, where the full set of keypoints is encoded into a conditioning vector to refine 3DGS attributes. This compensation supplies whole-face structural information to the underlying shape representation. Experiments under self-driven and cross-driven settings show that the proposed method improves visual quality, facial realism, and lip synchronization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-16 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.17422 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T18:03:09.538Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-16?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).