arXiv:2405.03650v4 Announce Type: replace-cross Abstract: We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content. Conventional image-generation systems can produce visually realistic outputs from limited scene descriptions, but the added content is usually implicit in the generator rather than represented as an inspectable intermediate structure. In contrast, GCE seeks to make scene enrichment explicit at the scene-r
This research builds on recent advances in conditional image generation, pushing towards more controllable and explicit scene representation, which is a key challenge in current AI development.
Explicit scene representation in image generation offers greater control, inspectability, and potential for integration into real-world applications requiring precise content creation and manipulation.
The explicit enrichment of sparse scene descriptions before rendering offers a new paradigm for generating rich visual content, moving beyond implicit generator black boxes.
- · AI content creators
- · Generative AI platforms
- · Robotics and simulation developers
- · Black-box generative AI models
- · Manual 3D artists (for routine tasks)
Conditional image generation systems will become more interpretable and controllable.
This improved control will facilitate the integration of AI-generated content into complex design, simulation, and agentic systems.
More structured and controllable content generation could accelerate the development of AI agents capable of reasoning visually and executing complex tasks in virtual or physical environments.
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Read at arXiv cs.LG