Cardioid Caustics Generation with Conditional Diffusion Models
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Despite the latest advances in generative neural techniques for producing photorealistic images, they lack generation of multi-bounce, high-frequency lighting effect like caustics. In this work, we tackle the problem of generating cardioid-shaped reflective caustics using diffusion-based generative models. We approach this problem as conditional image generation using a diffusion-based model conditioned with multiple images of geometric, material and illumination information as well as light property. We introduce a framework to fine-tune a pre-trained diffusion model and present results with visually plausible caustics.
Description
CCS Concepts: Computing methodologies → Artificial intelligence; Neural networks; Image-based rendering
@inproceedings{10.2312:egs.20251030,
booktitle = {Eurographics 2025 - Short Papers},
editor = {Ceylan, Duygu and Li, Tzu-Mao},
title = {{Cardioid Caustics Generation with Conditional Diffusion Models}},
author = {Uss, Wojciech and Kaliński, Wojciech and Kuznetsov, Alexandr and Anand, Harish and Kim, Sungye},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-268-4},
DOI = {10.2312/egs.20251030}
}