AutoFlow: Learning a Better Training Set for Optical Flow

• URL: http://arxiv.org/abs/2104.14544v1
• Date: Thu, 29 Apr 2021 17:55:23 GMT
• Title: AutoFlow: Learning a Better Training Set for Optical Flow
• Authors: Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, Ce Liu
• Abstract summary: AutoFlow is a method to render training data for optical flow. AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT.
• Score: 62.40293188964933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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