5 papers
Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation
Sangmin Han, Jinho Kim, Jinwoo Kim +2
Multi-exposure fusion (MEF) expands the luminance range beyond what a single exposure can capture. Combining images taken at different exposure levels requires handling geometric d…
RawGen: Learning Camera Raw Image Generation
Dongyoung Kim, Junyong Lee, Abhijith Punnappurath +4
Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more…
APT: Improving Diffusion Models for High Resolution Image Generation with Adaptive Path Tracing
Sangmin Han, Jinho Jeong, Jinwoo Kim +1
Latent Diffusion Models (LDMs) are generally trained at fixed resolutions, limiting their capability when scaling up to high-resolution images. While training-based approaches addr…
ORIDa: Object-centric Real-world Image Composition Dataset
Jinwoo Kim, Sangmin Han, Jinho Jeong +3
Object compositing, the task of placing and harmonizing objects in images of diverse visual scenes, has become an important task in computer vision with the rise of generative mode…
Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models
Jinho Jeong, Sangmin Han, Jinwoo Kim +1
In this paper, we propose LSRNA, a novel framework for higher-resolution (exceeding 1K) image generation using diffusion models by leveraging super-resolution directly in the laten…