PreciseCam: Precise Camera Control for Text-to-Image Generation (2501.12910v1)

Abstract: Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and artistic images. Unlike prior methods that rely on predefined shots, we rely solely on four simple extrinsic and intrinsic camera parameters, removing the need for pre-existing geometry, reference 3D objects, and multi-view data. We also present a novel dataset with more than 57,000 images, along with their text prompts and ground-truth camera parameters. Our evaluation shows precise camera control in text-to-image generation, surpassing traditional prompt engineering approaches. Our data, model, and code are publicly available at https://graphics.unizar.es/projects/PreciseCam2024.

• The paper presents a new framework that enables precise control of intrinsic and extrinsic camera parameters without relying on complex prompt engineering.
• It employs the Unified Spherical camera model to create a Perspective Field representation and integrates ControlNet with diffusion models, validated on a dataset of over 57,000 images.
• Experimental results show superior consistency in maintaining specified camera views compared to models like SDXL and Adobe Firefly, enhancing creative expression in digital art and video generation.

Precise Camera Control for Text-to-Image Generation

The paper "Precise Camera Control for Text-to-Image Generation," authored by Edurne Bernal-Berdun et al., addresses a notable gap in the domain of text-to-image (T2I) generative models: the precise control of camera parameters. Despite significant advancements in T2I capabilities, these models often lack precise camera manipulation options, limiting their utility for creative tasks requiring nuanced camera perspectives. This paper presents a novel framework, named "black," that enhances the expressive potential of generative models by specifying intrinsic and extrinsic camera parameters.

Methodology and Key Contributions

The research introduces a solution that bypasses the reliance on traditional prompt engineering, which is both laborious and imprecise. The authors identify four key camera parameters: roll, pitch, vertical field of view (vFoV), and distortion, which collectively define any camera view. By integrating control over these parameters, the model enables detailed manipulation of the image generation process. This control does not necessitate predefined shots or complex 3D data, differentiating black from prior approaches. Instead, they utilize the Unified Spherical (US) camera model to translate these parameters into a Perspective Field (PF) representation, which acts as a supplementary input condition to guide the generative process.

An essential innovation in their approach is the seamless integration of ControlNet with the diffusion model, allowing the image generation process to be guided by the PF representation. The method is trained on a newly created dataset comprising over 57,000 images, each annotated with text prompts and corresponding ground-truth camera parameters. This dataset is pivotal, offering a broad range of camera configurations to train the model for precise camera view specification.

Experimental Evaluation and Results

The experimental results in this paper demonstrate significant advancements over current state-of-the-art methods. The authors present comparative analyses with baseline models, including Stable Diffusion XL (SDXL) and Adobe Firefly, highlighting the inability of existing prompts or tag-based systems to provide fine control over camera angles and viewpoints. Black consistently generates images that accurately reflect the specified camera parameters, maintaining high alignment with the text prompt as evinced by comparable CLIP and BLIP scores.

Their model shows robustness by sustaining camera perspective consistency across varying initial noise inputs and minor variations in the text prompt. This stability is crucial for applications where precise visual consistency is necessary across different scenes or compositions.

Implications and Future Directions

The ability to precisely control camera views in T2I models enhances the versatility of generative AI, particularly in fields requiring nuanced creative expression, such as digital art, video production, and virtual reality. By offering accurate camera manipulation tools, black opens new possibilities for artists and designers to explore unique perspectives and compositions without pre-existing geometric data.

Moreover, the approach can be extended to video generation, assisting in setting initial camera conditions that guide subsequent frames, thus integrating seamlessly with existing video generation techniques that focus on relative camera movements. The paper also hints at the potential for integrating black with other control-oriented models or techniques, paving the way for composite methods that could further elevate the standards for synthesized visual content.

Conclusion

This paper significantly advances the control capabilities of T2I models, allowing for fine-tuned manipulation of camera parameters without cumbersome prompt engineering. While the paper thoroughly addresses a critical limitation of existing models, it also points towards future challenges and research directions, inviting further exploration into integrating such precise controls within multi-modal generative frameworks. This contribution underscores a compelling step forward for the practical application of AI in creative and technical fields.