Generating metamers of human scene understanding

ICLROral2026

Authors
Ritik Raina, Abe Leite, Alexandros Graikos, Seoyoung Ahn, Dimitris Samaras, Greg Zelinsky
Affiliation
State University of New York at Stony Brook
Venue
ICLR 2026
Track
Oral

TL;DR

Human vision combines low-resolution “gist” information from the visual periphery with sparse but high-resolution information from fixated locations to construct a coherent understanding of a visual scene. In this paper, we introduce MetamerGen, a tool for generating scenes that are aligned with latent human scene representations.

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