  Existing approaches to unsupervised video instance segmentation typically
rely on motion estimates and experience difficulties tracking small or
divergent motions. We present VideoCutLER, a simple method for unsupervised
multi-instance video segmentation without using motion-based learning signals
like optical flow or training on natural videos. Our key insight is that using
high-quality pseudo masks and a simple video synthesis method for model
training is surprisingly sufficient to enable the resulting video model to
effectively segment and track multiple instances across video frames. We show
the first competitive unsupervised learning results on the challenging
YouTubeVIS-2019 benchmark, achieving 50.7% APvideo^50 , surpassing the previous
state-of-the-art by a large margin. VideoCutLER can also serve as a strong
pretrained model for supervised video instance segmentation tasks, exceeding
DINO by 15.9% on YouTubeVIS-2019 in terms of APvideo.
