Ontology-Guided Diffusion for Zero-Shot Visual Sim2Real Transfer
Summary: arXiv:2603.18719v2 Announce Type: replace-cross
Abstract
Bridging the simulation-to-reality (sim2real) gap remains challenging as labelled real-world data is scarce. Existing diffusion-based approaches rely on unstructured prompts or statistical alignment, which do not capture the structured factors that make images look real. We introduce Ontology-Guided Diffusion (OGD), a neuro-symbolic zero-shot sim2real image translation framework that represents realism as structured knowledge.
Introduction
The challenge of transferring visual information from simulated environments to real-world applications is a pressing issue in the field of artificial intelligence. Traditional methods often struggle due to the lack of sufficient labelled data and the inherent differences between synthetic and real images. OGD addresses these issues by leveraging structured knowledge to enhance the realism of generated images.
Key Features of Ontology-Guided Diffusion (OGD)
- Ontology Decomposition: OGD decomposes the concept of realism into an ontology of interpretable traits, such as lighting and material properties. This structured approach allows for a more nuanced understanding of what makes an image appear realistic.
- Knowledge Graph: The relationships between different traits are encoded in a knowledge graph, facilitating the inference of trait activations from synthetic images.
- Graph Neural Network: A graph neural network is employed to produce a global embedding that captures the essential features of the image based on its trait activations.
- Symbolic Planning: A symbolic planner utilizes the traits outlined in the ontology to compute a consistent sequence of visual edits necessary to minimize the realism gap between synthetic and real images.
- Instruction-Guided Diffusion Model: The graph embedding conditions a pretrained instruction-guided diffusion model through cross-attention, effectively guiding the image generation process.
Performance and Results
Across multiple benchmarks, OGD has demonstrated superior performance compared to existing state-of-the-art diffusion methods in sim2real image translations. The graph-based embeddings produced by OGD have shown a heightened ability to distinguish between real and synthetic imagery, enabling more accurate translations that maintain visual fidelity.
Conclusion
The introduction of Ontology-Guided Diffusion marks a significant advancement in the field of zero-shot visual sim2real transfer. By explicitly encoding the structure of realism, OGD paves the way for more interpretable, data-efficient, and generalizable approaches to image translation. This framework not only addresses existing limitations in the field but also opens up new avenues for research and application in artificial intelligence.