Learning Continuous 3D Words for Text-to-Image Generation (2402.08654v1)

Abstract: Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction or non-rigid shape change. In this paper, we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words. These attributes can, for example, be represented as sliders and applied jointly with text prompts for fine-grained control over image generation. Given only a single mesh and a rendering engine, we show that our approach can be adopted to provide continuous user control over several 3D-aware attributes, including time-of-day illumination, bird wing orientation, dollyzoom effect, and object poses. Our method is capable of conditioning image creation with multiple Continuous 3D Words and text descriptions simultaneously while adding no overhead to the generative process. Project Page: https://ttchengab.github.io/continuous_3d_words

Integrating 3D Awareness into Text-to-Image Models with Continuous 3D Words

Introduction to Continuous 3D Words

Continuous 3D Words presents an innovative method to incorporate fine-grained, 3D-aware controls into text-to-image generators, enhancing image customization with minimal training samples. This approach fosters advanced manipulation of images through user-friendly prompts, bridging the gap between the expressive freedom of photography and the generative capabilities of AI, without extensive 3D scene creation.

Methodology

At the core of this method lies the concept of Continuous 3D Words—specific tokens allowing seamless manipulation of 3D attributes such as illumination direction and non-rigid shape changes directly through text prompts. This method's efficacy stems from a two-pronged strategy:

1. Continuous Vocabulary Learning:
   • A novel algorithm underpins learning this vocabulary, ensuring attributes across a continuous spectrum are easily learned without resorting to numerous discrete tokens.
   • Employing a simple MLP for this purpose facilitates interpolation during inference, thus generating continuous control over attributes.
2. Training Strategy:
   • The method employs a two-stage training protocol involving the Dreambooth approach for learning object identities and sequential learning of attribute values, ensuring attributes are decoupled from object identities.
   • Augmentation strategies, including ControlNet modifications for background and texture variation, bolster the model's generalization capabilities.

Evaluation and Insights

Comprehensive qualitative and quantitative evaluations underscore the superiority of Continuous 3D Words over traditional ControlNet-based methods, particularly in generating images that accurately reflect user-defined 3D attributes. Through user studies, this approach has demonstrated significant advancements in generating aesthetically pleasing images that conform closely to specified attributes. Moreover, the methodology showcases promising application in real-world image editing, indicating its potential for broad adoption in creative industries.

Implications and Future Directions

Theoretical Implications:

• Introduces a significant advancement in text-to-image generation by operationalizing 3D awareness with minimal computational overhead.
• Highlights the capacity for deep learning models to understand and manipulate nuanced, continuous attributes beyond the scope of existing datasets.

Practical Applications:

• Opens avenues for professionals and enthusiasts alike to exert granular control over the aesthetics and composition of generated images, closely mimicking the flexibility of photography.
• Offers a foundational step toward intuitive, easy-to-use interfaces for generating complex 3D scenes and objects with textual prompts, democratizing access to high-fidelity image generation.

Speculating on Future Developments:

• The integration of Continuous 3D Words with larger, more diverse datasets could further enhance model performance and general applicability across various domains.
• Continuous exploration might yield methods for automatically identifying and leveraging relevant 3D attributes from existing large-scale text-to-image models without explicit attribute training.
• Advancements in understanding and manipulating the interplay between multiple 3D attributes could lead to breakthroughs in animated or interactive image generation, expanding beyond static imagery to dynamic visual content creation.

In conclusion, Continuous 3D Words mark a pivotal step toward enriching text-to-image generation with 3D awareness, offering nuanced control and opening new horizons in digital content creation. This research paves the way for further explorations into the synergy between textual prompts and 3D visual attributes, heralding a future where detailed and realistic image generation is both accessible and versatile.