Publication / 2026.02

CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models

A training technique that supervises attention maps using geometric correspondence, reducing training iterations by half while achieving superior multi-view generation quality.

Role Research Contributor
Venue CVPR 2026
Date February 2026
Authors Minkyung Kwon*, Jinhyeok Choi*, Jiho Park*, Seonghu Jeon, Jinhyuk Jang, Junyoung Seo, Minseop Kwak, Jin-Hwa Kim†, Seungryong Kim†
Paper Project Page arXiv PDF Code
CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models
Contents
  1. Abstract
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Abstract

We present CAMEO, a method to align correspondence and attention for multi-view diffusion models. By supervising attention maps with geometric correspondence during training, we ensure that attention across different views remains consistent with underlying 3D geometry. This achieves significantly improved multi-view consistency while reducing training iterations by half.

#3D Vision #Diffusion Models #Generative AI