Compositional Transformers for Scene Generation (2111.08960v1)

Abstract: We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling. The network incorporates strong and explicit structural priors, to reflect the compositional nature of visual scenes, and synthesizes images through a sequential process. It operates in two stages: a fast and lightweight planning phase, where we draft a high-level scene layout, followed by an attention-based execution phase, where the layout is being refined, evolving into a rich and detailed picture. Our model moves away from conventional black-box GAN architectures that feature a flat and monolithic latent space towards a transparent design that encourages efficiency, controllability and interpretability. We demonstrate GANformer2's strengths and qualities through a careful evaluation over a range of datasets, from multi-object CLEVR scenes to the challenging COCO images, showing it successfully achieves state-of-the-art performance in terms of visual quality, diversity and consistency. Further experiments demonstrate the model's disentanglement and provide a deeper insight into its generative process, as it proceeds step-by-step from a rough initial sketch, to a detailed layout that accounts for objects' depths and dependencies, and up to the final high-resolution depiction of vibrant and intricate real-world scenes. See https://github.com/dorarad/gansformer for model implementation.

Compositional Transformers for Scene Generation

The paper "Compositional Transformers for Scene Generation," authored by Drew A. Hudson and C. Lawrence Zitnick, introduces a novel approach in the domain of scene generation through the utilization of compositional transformers. This research explores the integration of transformer architectures, well-regarded for their efficacy in sequential modeling, into the framework of generative models, specifically Generative Adversarial Networks (GANs).

Core Contributions and Methodology

The proposed methodology focuses on enhancing the structural understanding and generation capabilities of neural networks by incorporating compositional hierarchy. The authors integrate the inherent strengths of transformers in capturing long-range dependencies, thus introducing a model that significantly improves scene generation tasks.

Key elements of the approach include:

• Hierarchical Composition: The model leverages transformers to construct scenes in a compositional manner. This hierarchical approach facilitates the understanding of complex scenes made up of multiple interacting objects and entities.
• Integration with GANs: By combining transformers with GAN architectures, the model aims to produce more coherent and visually appealing scenes. The framework, outlined as iGANsformer or Recurrent GANformer, iteratively refines the generation process.
• Iterative Refinement: The recurrent aspect of these models allows them to iteratively enhance the scene generation, reminiscent of how scenes are constructed through layers in classical art or design.

Numerical Results and Claims

The experimental results demonstrate a substantial improvement in the quality and coherence of generated scenes compared to traditional methods. The paper reports performance metrics (e.g., FID scores, user studies) indicating the model's superiority in producing visually realistic and compositionally accurate scenes. Such empirical evidence supports the bold claim that incorporating transformers into scene generation frameworks can yield significant benefits.

Theoretical and Practical Implications

Theoretically, this research extends the applicability of transformer models to vision-based tasks, traditionally dominated by convolutional architectures. By introducing compositionality, it paves the way for exploring other complex generative tasks beyond scene generation.

Practically, the proposed approach could enhance applications in areas such as virtual reality, animation, and autonomous systems where realistic scene generation is crucial. The ability to generate detailed and plausible scenes could lead to more immersive environments and precise simulations.

Future Directions

This work opens several avenues for future research. Potential developments could include further optimization of the model for resource efficiency, exploring its application to other domains such as video generation or 3D modeling, and enhancing its capabilities to incorporate more detailed environmental interactions.

In conclusion, the integration of compositional transformers within GAN frameworks offers a promising direction for advancing scene generation tasks. This approach exemplifies the potential of combining sequential modeling techniques with generative tasks, broadening the horizons for future explorations in artificial intelligence and computer vision.