Camera Control for Text-to-Image Generation via Learning Viewpoint Tokens

Current text-to-image models struggle to express precise camera control using natural language alone. In this work, we present a framework for precise camera control with global scene understanding in text-to-image generation by learning parametric camera tokens. We fine-tune a unified multimodal model for viewpoint-conditioned text-to-image generation on a curated dataset that combines high-volume rendered images for geometric supervision with low-volume photorealistic augmentations for appearance diversity. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art accuracy across all camera parameters while preserving image quality and prompt fidelity. Unlike prior methods that overfit to object-specific appearance correlations, our viewpoint tokens learn factorized geometric representations that transfer to unseen object categories. Our work shows that text-vision latent spaces can be endowed with explicit 3D camera structure, offering a pathway toward geometrically-aware prompts for text-to-image generation.