Leveraging Text-To-Image Diffusion Models for Consistent Set-to-Set Generation| Voxel51

Image collections are humans' primary way of capturing the world, yet advances in generative editing remain largely inapplicable to this modality. We address this gap by introducing Match-and-Fuse - a zero-shot, training-free method for consistent set-to-set generation from image collections that share a common visual element but differ in viewpoint, capture time, and surrounding content. Our key idea is a unified graph-based framework that combines dense correspondences with an emergent prior in text-to-image diffusion models to generate coherent canvases. We achieve state-of-the-art consistency and visual quality, and unlock new creative capabilities for content generation. Kate Feingold is a PhD student in Computer Vision at the Weizmann Institute of Science. Her research sits at the intersection of generative models, 3D/4D perception, and multimodal learning, focusing on problems where vision meets other modalities or paradigms in creative tasks. Previously, she spent over six years working in computer vision and deep learning across industry, spanning Neural Architecture Search, UAV navigation, action recognition, and video understanding. Paper: Match-and-Fuse: Consistent Generation from Unstructured Image Sets https://arxiv.org/pdf/2511.22287 #FiftyOne #Voxel51 #DataCuration #AIAgents #ComputerVision #MCP #YOLO #DataCentricAI #skills #agenticai