Text2Scene: Generating Compositional Scenes from Textual Descriptions (1809.01110v3)

Abstract: In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, including cartoon-like scenes, object layouts corresponding to real images, and synthetic images. Our method is not only competitive when compared with state-of-the-art GAN-based methods using automatic metrics and superior based on human judgments but also has the advantage of producing interpretable results.

• The paper introduces a unified framework that sequentially and interpretably generates diverse compositional scene representations directly from text.
• It employs dual attention mechanisms to process object and attribute details, ensuring accurate placement and visual coherence.
• Empirical evaluations demonstrate superior object layout precision and perceptual quality compared to traditional GAN-based models.

Overview of Text2Scene: Generating Compositional Scenes from Textual Descriptions

The paper presents "Text2Scene", an innovative computational model engineered to generate various types of compositional scene representations based on natural language descriptions. The framework distinguishes itself by eschewing the frequently utilized Generative Adversarial Networks (GANs) in favor of a more interpretable, sequential generation process. This approach sequentially generates objects and their attributes—such as location, size, and appearance—at each time step. It accomplishes this by attentively processing distinct segments of the input text in parallel with evaluating the current composition of the generated scene.

Key contributions of the paper include:

1. Unified Framework: Text2Scene is shown to efficiently generate different forms of scene representations across three distinct applications: cartoon-like scenes, object layouts aligned with real images, and synthetic image compositions. It demonstrates that a single model can adapt to different scene generation tasks with minimal modifications, addressing challenges unique to each task.
2. Interpretable Model Design: As opposed to the adversarial training involved in GAN-based approaches, this framework allows for the generation of scenes in a manner that is interpretable at every step, making it easier to understand and debug the model's decisions.
3. Attention Mechanisms: Text2Scene incorporates both object-level and attribute-level attention mechanisms that significantly improve the grounding of objects and their attributes as reflected in the model's ability to attend to relevant portions of the textual input effectively.

In empirical evaluations, Text2Scene shows competitive automatic performance and garners superior human judgment evaluations compared to state-of-the-art GAN-based models. Specifically, in the Abstract Scenes dataset, it surpasses prior models in most metrics relating to object and attribute placement precision as well as expression recognition, demonstrating its capability to maintain semantic integrity in complex compositional scenes.

The theoretical implications of this research are profound, highlighting the potential for sequential models with attention mechanisms in tasks traditionally dominated by GANs. Practically, this could elevate applications in automated graphic design, natural language-driven image retrieval, and dynamic scene synthesis. The model's architecture also lays the groundwork for efficient transfer learning across varied scene generation tasks, suggesting pathways for further exploration in artificial intelligence research domains focused on multimodal learning and visual semantics.

As for future developments, research could explore refining the attribute decoder to better capture nuances in object relations and further improving computational efficiency to enable real-time scene generation from textual input. There is also potential to integrate this framework with traditional unsupervised learning techniques to minimize dependency on labeled datasets, pushing the frontier further in automated scene synthesis technologies.