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SlerpFlow achieves high-precision image inversion and editing while maintaining the efficiency of first-order solvers, transforming how we approach flow-based diffusion models.
A novel data synthesis approach enables MLLMs to achieve robust 3D spatial reasoning, rivaling traditional 3D models without expensive pre-training.
For the first time, a scaling law for quadruped motion tracking reveals that performance consistently improves with larger training datasets, unlocking new capabilities in robotic locomotion.
