Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis
This addresses a limitation in generative tools for professional photography by enabling precise camera control, though it is incremental as it builds on existing text-to-image synthesis methods.
The paper tackles the problem of generating scene-consistent images with specific camera settings like lens focal lengths, which existing text-to-image models fail to do, and introduces a framework that achieves significantly more realistic and consistent results compared to state-of-the-art models such as Stable Diffusion 3 and FLUX.
Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a specific camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the generator will not be able to interpret and generate scene-consistent images. This limitation not only hinders the adoption of generative tools in professional photography but also highlights the broader challenge of aligning data-driven models with real-world physical settings. In this paper, we introduce Generative Photography, a framework that allows controlling camera intrinsic settings during content generation. The core innovation of this work are the concepts of Dimensionality Lifting and Differential Camera Intrinsics Learning, enabling smooth and consistent transitions across different camera settings. Experimental results show that our method produces significantly more scene-consistent photorealistic images than state-of-the-art models such as Stable Diffusion 3 and FLUX. Our code and additional results are available at https://generative-photography.github.io/project.