arXiv Artificial Intelligence

WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation

1 hour ago 30.09.2026 07:00 👁 1 views
WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation

Quick summary

arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation, achieving high-fidelity animation of multiple interacting subjects remains a challenge. Many existing approaches rely on explicit motion representations such as 2D skeletons or parametric body meshes and struggle to preserve identity-motion binding under inter-person occlusion. To address this limitation, we propose WeLike2Party, a multi-human animation framework built on direct in-context video c

Key takeaways

  • arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image.
  • Despite remarkable progress in video generation, achieving high-fidelity animation of multiple interacting subjects remains a challenge.
  • Many existing approaches rely on explicit motion representations such as 2D skeletons or parametric body meshes and struggle to preserve identity-motion binding under inter-person occlusion.

Why it matters

“WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗