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EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold — arXiv:2609.35616

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.35616
License (as archived): CC0-1.0
Archived: 2026-10-07T23:54:53.839Z
SHA-256 of the archived text: 3e6fd1274a6df842ed89d3486ead3d869e6a6310681389429ec2392bdb01e157
Stable URL of this page: https://metraipolis.com/documento/dc_abe095cb0dfe

How to cite

EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold — arXiv:2609.35616 — arXiv (en), https://arxiv.org/abs/2609.35616 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abe095cb0dfe (Metraipolis archive dc_abe095cb0dfe, sha256 3e6fd1274a6df842ed89d3486ead3d869e6a6310681389429ec2392bdb01e157)

BibTeX

@misc{dcabe095cb0dfe,
  title = {EvolvingAvatar: Interactive 3D Head Generation That Adapts as Conversations Unfold — arXiv:2609.35616},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.35616},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abe095cb0dfe; sha256 3e6fd1274a6df842ed89d3486ead3d869e6a6310681389429ec2392bdb01e157},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.35616. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation. Existing generators use incoming observations as context but keep their parameters fixed, leaving conversational patterns unused as a learning signal. We introduce EvolvingAvatar, a causal generator that uses test-time training to adapt to user face video and dyadic audio during interaction. Its dyadic context prediction objective provides a self-supervised learning signal from audiovisual context without target motion labels at test time. Persistent fast weights accumulate these updates within each conversation to guide motion generation, while transient jaw adaptation responds to current audiovisual context. Predicted speech activity controls how persistent adaptation guides motion. We also introduce InterHead-Bench, a unified 455.95-hour benchmark built from single-view and dual-view conversation videos. Experiments show improved conversational motion statistics over strong baselines. On the hardest out-of-distribution split, generation improves as conversations unfold, reducing mismatch with recorded user-avatar expression statistics by up to 11.1% from the first interval.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.35616 — 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 2026-10-07T23:29:27.608Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).