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Shanghai Jiao Tong University
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TEASR achieves seamless any-step sampling and outperforms traditional methods, all while eliminating the need for bulky auxiliary models.
Large-scale generative models struggle with low-level vision tasks, revealing critical performance gaps that conventional metrics fail to capture.
Achieve state-of-the-art dynamic 3D reconstruction from sparse views by intelligently fusing explicit 3D geometry with generative priors, avoiding common pitfalls like structural drift and temporal inconsistency.
