@inproceedings{493c02e5c13e450ea602fd250baee73c,
title = "Curved Diffusion: A Generative Model with Optical Geometry Control",
abstract = "State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlooked is the specific camera geometry used during image capture. The influence of different optical systems on the final scene appearance is frequently overlooked. This study introduces a framework that intimately integrates a text-to-image diffusion model with the particular lens geometry used in image rendering. Our method is based on a per-pixel coordinate conditioning method, enabling the control over the rendering geometry. Notably, we demonstrate the manipulation of curvature properties, achieving diverse visual effects, such as fish-eye, panoramic views, and spherical texturing using a single diffusion model.",
keywords = "Camera Geometry, Diffusion Model, Stereo Generation",
author = "Andrey Voynov and Amir Hertz and Moab Arar and Shlomi Fruchter and Daniel Cohen-Or",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",
year = "2024",
doi = "10.1007/978-3-031-72980-5_9",
language = "אנגלית",
isbn = "9783031729799",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "149--164",
editor = "Ale{\v s} Leonardis and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and G{\"u}l Varol",
booktitle = "Computer Vision – ECCV 2024 - 18th European Conference, Proceedings",
address = "גרמניה",
}