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We present InverseBench, a framework for benchmarking plug-and-play diffusion approaches for inverse problems in physical sciences. (#ICLR2025 Spotlight) PnP diffusion approaches are attractive for scientific inverse problems because they offer flexibility in incorporating

TLDR: if you can train video diffusion priors, then solving video inverse problems can be done much simpler and more accurately than heavily engineered approaches based on image diffusion priors. Examples below for black hole video reconstruction and dynamic MRI.

If you're attending CVPR 2025, please join our talk and poster session on Sunday, June 15 (the final day)! 🗣️ Oral Session 5A: Generative AI 📍 Karl Dean Grand Ballroom | 🕘 9:00 AM 📌 Poster Session #200 📍 Exhibit Hall D | 🕥 10:30 AM – 12:30 PM