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End-to-end reinforcement learning can discover better image processing pipelines than step-wise methods, while also being more computationally efficient.
Synthetic motion data, when represented as optical flow, unlocks a new level of realism and control in video diffusion models, surpassing the limitations of real-world datasets.
