Manipulating lighting conditions in images post-capture is challenging. Traditional approaches rely on 3D graphics methods that reconstruct scene geometry and properties from multiple captures before simulating new lighting using physical illumination models. Though these techniques provide explicit control over light sources, recovering accurate 3D models from single images remains a problem that frequently results in…

LLMs have made significant strides in language-related tasks such as conversational AI, reasoning, and code generation. However, human communication extends beyond text, often incorporating visual elements to enhance understanding. To create a truly versatile AI, models need the ability to process and generate text and visual information simultaneously. Training such unified vision-language models from scratch…

Text-to-image (T2I) generation has evolved to include subject-driven approaches, which enhance standard T2I models by incorporating reference images alongside text prompts. This advancement allows for more precise subject representation in generated images. Despite the promising applications, subject-driven T2I generation faces a significant challenge of lacking reliable automatic evaluation methods. Current metrics focus either on text-prompt…