CVPR 2025 Tutorial onScalable Generative Models in Computer Vision |
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Location Room 202 B |
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CVPR 2025 Tutorial onScalable Generative Models in Computer Vision |
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Location Room 202 B |
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Generative models have emerged as a transformative force in computer vision, enabling breakthroughs in image, video, and 3D content synthesis. Recent advancements in model architectures and generative frameworks have driven unprecedented scalability, allowing models to handle larger datasets, longer context lengths, and more complex distributions. This tutorial will provide a comprehensive discussion of these advancements, focusing on frontier techniques for scaling generative models and their applications to video synthesis, 3D reconstruction, and virtual world simulation. Attendees will gain insights into the design principles behind scalable models, learn about key technical innovations, and understand the broader implications for the future of computer vision. By addressing both theoretical and practical aspects, this tutorial aims to equip researchers with the knowledge to explore, build, and deploy next-generation scalable generative models.
| Time | Session |
|---|---|
| 9:00 - 9:10 | Opening Remarks |
| 9:10 - 10:00 | Saining Xie Generating More with Less: A Representation Learning Perspective |
| 10:00 - 10:50 | Deepti Ghadiyaram Advancing Safety, Alignment, and Interpretability in Generative Media |
| 10:50 - 11:40 | Jiatao Gu Scalable Normalizing Flows for Visual Generation |
| 11:40 - 2:00 | Lunch Break |
| 2:00 - 2:50 | Kaiming He Towards End-to-End Generative Modeling |
| 2:50 - 3:40 | Sherry Yang Scaling World Models for Agents |
| 3:40 - 4:00 | Coffee Break |
| 4:00 - 4:50 | Arash Vahdat From Hundreds to One: On Accelerated Sampling from Diffusion Models |
| 4:50 - 5:00 | Conclusion |
Contact: Willis Ma, Oscar Michel, Saining Xie
