AutoFlow: Learning a Better Training Set for Optical Flow

Deqing Sun; Daniel Vlasic; Charles Herrmann; Varun Jampani; Michael Krainin; Huiwen Chang; Ramin Zabih; William T. Freeman; Ce Liu

arXiv · 2104.14544

AutoFlow: Learning a Better Training Set for Optical Flow

Deqing Sun·Daniel Vlasic·Charles Herrmann·Varun Jampani·Michael Krainin·Huiwen Chang·Ramin Zabih·William T. Freeman·Ce Liu

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Abstract

Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at https://autoflow-google.github.io .

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Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, Ce Liu. 2021-04-29. AutoFlow: Learning a Better Training Set for Optical Flow. https://arxiv.org/abs/2104.14544

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