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Taming foundation models for visual concept learning and 4D modeling

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If you have a question about this talk, please contact Elliott Wu .

In this talk, I will present our recent work on leveraging foundation models for open-world visual concept learning and 4D modeling. First, I will discuss how we repurpose vision foundation models for continual category discovery by learning a flexible Gaussian mixture prompt pool. Next, I will introduce our approach to automatically extracting visual concepts, both at the object and intrinsic levels, using Stable Diffusion models. Finally, I will share our work on high-quality 4D generation by effectively harnessing video diffusion models, enabling temporally and spatially consistent content creation with 4D Gaussian splatting.

This talk is part of the Computer Vision Seminars series.

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