Recent advances in generative modeling leveraging generative adversarial networks, auto-regressive models, neural fields and diffusion models have enabled the synthesis of near photorealistic images, drastically increasing the visibility and popularity of generative modeling across the computer vision research community. However, these impressive advances in generative modeling have not yet found wide adoption in computer vision for visual recognition tasks. In this workshop, we aim to bring together researchers from the fields of image synthesis and computer vision to facilitate discussions and progress at the intersection of those two subfields. We investigate the question: "How can visual recognition benefit from the advances in generative image modeling?". We invite a diverse set of experts to discuss their recent research results and future directions for generative modeling and computer vision, with a particular focus on the intersection between image synthesis and visual recognition. We hope this workshop will lay the foundation for future development of generative models for computer vision tasks.

Submission site: https://cmt3.research.microsoft.com/GCV2023/.
Author kit: CVPR Author KIT.
We invite submissions of both short and long papers (4 pages and 8 pages respectively excluding references). The long papers will be included in the proceedings of CVPR. Potential topics include but are not limited to:

• Advances in generative image models
• Inversion of generative image models
• Training computer vision with realistic synthetic images
• Benchmarking computer vision with generative models
• Analysis-by-synthesis / render-and-compare approaches for visual recognition
• Self-supervised learning with generative models
• Adversarial attacks and defenses with generative models
• Out-of-distribution generalization and detection with generative models
• Ethical considerations in generative modeling, dataset and model biases