Recent advances in diffusion models have substantially improved text-driven image editing. However, existing frameworks based on discrete textual tokens struggle to support continuous control over camera parameters and smooth transitions in visual effects. In this paper, we present CamEdit, a diffusion-based framework for photorealistic image editing that enables continuous and semantically meaningful manipulation of common camera parameters such as aperture and shutter speed. It contains over 50k image pairs combining real and synthetic data with dense camera parameter variations across diverse scenes. Extensive experiments demonstrate that CamEdit enables flexible, consistent, and high-fidelity image editing, achieving state-of-the-art performance in camera-aware visual manipulation and fine-grained photographic control.
@article{qin2026camedit,
title = {CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing},
author = {Qin, Xinran and Wang, Zhixin and Li, Fan and Chen, Haoyu and Pei, Renjing and Li, Wenbo and Cao, Xiaochun},
journal = {Advances in Neural Information Processing Systems},
volume = {38},
pages = {114152--114171},
year = {2026}
}