Discovering and using Spelke segments
Authors: name: Rahul Venkatesh, name: Klemen Kotar, name: Lilian Naing Chen, name: Seungwoo Kim, name: Luca Thomas Wheeler, name: Jared Watrous, name: Ashley Xu, name: Gia Ancone, name: Wanhee Lee, name: Honglin Chen, name: Daniel Bear, name: Stefan Stojanov, name: Daniel Yamins

Segments in computer vision are often defined by semantic considerations and
are highly dependent on category-specific conventions. In contrast,
developmental psychology suggests that humans perceive the world in terms of
Spelke objects--groupings of physical things that reliably move together when
acted on by physical forces. Spelke objects thus operate on category-agnostic
causal motion relationships which potentially better support tasks like
manipulation and planning. In this paper, we first benchmark the Spelke object
concept, introducing the SpelkeBench dataset that contains a wide variety of
well-defined Spelke segments in natural images. Next, to extract Spelke
segments from images algorithmically, we build SpelkeNet, a class of visual
world models trained to predict distributions over future motions. SpelkeNet
supports estimation of two key concepts for Spelke object discovery: (1) the
motion affordance map, identifying regions likely to move under a poke, and (2)
the expected-displacement map, capturing how the rest of the scene will move.
These concepts are used for "statistical counterfactual probing", where diverse
"virtual pokes" are applied on regions of high motion-affordance, and the
resultant expected displacement maps are used define Spelke segments as
statistical aggregates of correlated motion statistics. We find that SpelkeNet
outperforms supervised baselines like SegmentAnything (SAM) on SpelkeBench.
Finally, we show that the Spelke concept is practically useful for downstream
applications, yielding superior performance on the 3DEditBench benchmark for
physical object manipulation when used in a variety of off-the-shelf object
manipulation models.