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SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

Austin Stone
Daniel Maurer
Alper Ayvaci
Rico Jonschkowski
Computer Vision and Pattern Recognition (CVPR) (2021)


We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% (over the prior best method UFlow) and even outperforms several supervised approaches such as PWC-Net and FlowNet2. Our method integrates architecture improvements from supervised optical flow, i.e. the RAFT model, with new ideas for unsupervised learning that include a sequence-aware self-supervision loss, a technique for handling out-of-frame motion, and an approach for learning effectively from multi-frame video data while still only requiring two frames for inference.

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