arXiv Artificial Intelligence

TransPhy: Visual In-Context Learning for Physically Grounded Image Editing

14 days ago 27.08.2026 07:00 👁 1 views
TransPhy: Visual In-Context Learning for Physically Grounded Image Editing

Quick summary

arXiv:2608.24119v1 Announce Type: cross Abstract: Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text. However, existing visual in-context learning (VICL) methods primarily focus on appearance-level relation transfer and provide limited support for physically grounded transformations, whose outcomes depend on material properties, geometry, object interactions, and environmental conditions. Given a source--target exemplar pair and a query image, physically grounded VICL requires a model to infer the demonst

Key takeaways

  • arXiv:2608.24119v1 Announce Type: cross Abstract: Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text.
  • However, existing visual in-context learning (VICL) methods primarily focus on appearance-level relation transfer and provide limited support for physically grounded transformations, whose outcomes depend on material properties, geometry, object interactions, and environmental conditions.
  • Given a source--target exemplar pair and a query image, physically grounded VICL requires a model to infer the demonst

Why it matters

“TransPhy: Visual In-Context Learning for Physically Grounded Image Editing” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗