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The visual world can be parsimoniously characterized in terms of distinctentities with sparse interactions. Discovering this compositional structure indynamic visual scenes has proven challenging for end-to-end computer visionapproaches unless explicit instance-level supervision is provided. Slot-basedmodels leveraging motion cues have recently shown great promise in learning torepresent, segment, and track objects without direct supervision, but theystill fail to scale to complex real-world multi-object videos. In an effort tobridge this gap, we take inspiration from human development and hypothesizethat information about scene geometry in the form of depth signals canfacilitate object-centric learning. We introduce SAVi++, an object-centricvideo model which is trained to predict depth signals from a slot-based videorepresentation. By further leveraging best practices for model scaling, we areable to train SAVi++ to segment complex dynamic scenes recorded with movingcameras, containing both static and moving objects of diverse appearance onnaturalistic backgrounds, without the need for segmentation supervision.Finally, we demonstrate that by using sparse depth signals obtained from LiDAR,SAVi++ is able to learn emergent object segmentation and tracking from videosin the real-world Waymo Open dataset.

SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos