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

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