Multi-Object Discovery by Low-Dimensional Object Motion

Understand

Recent work in unsupervised multi-object segmentation shows impressive results by predicting motion from a single image despite the inherent ambiguity in predicting motion without the next image.

• On the other hand, the set of possible motions for an image can be constrained to a low-dimensional space by considering the scene structure and moving objects in it.
• We propose to model pixel-wise geometry and object motion to remove ambiguity in reconstructing flow from a single image.
• Specifically, we divide the image into coherently moving regions and use depth to construct flow bases that best explain the observed flow in each region.