Understanding tools: Task-oriented object modeling, learning and recognition

Yixin Zhu, Yibiao Zhao, Song-Chun Zhu
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we present a new framework -taskoriented modeling, learning and recognition which aims at understanding the underlying functions, physics and causality in using objects as "tools". Given a task, such as, cracking a nut or painting a wall, we represent each object, e.g. a hammer or brush, in a generative spatiotemporal representation consisting of four components: i) an affordance basis to be grasped by hand; ii) a functional basis to act on a target object (the nut), iii) the
more » ... ined actions with typical motion trajectories; and iv) the underlying physical concepts, e.g. force, pressure, etc. In a learning phase, our algorithm observes only one RGB-D video, in which a rational human picks up one object (i.e. tool) among a number of candidates to accomplish the task. From this example, our algorithm learns the essential physical concepts in the task (e.g. forces in cracking nuts). In an inference phase, our algorithm is given a new set of objects (daily objects or stones), and picks the best choice available together with the inferred affordance basis, functional basis, imagined human actions (sequence of poses), and the expected physical quantity that it will produce. From this new perspective, any objects can be viewed as a hammer or a shovel, and object recognition is not merely memorizing typical appearance examples for each category but reasoning the physical mechanisms in various tasks to achieve generalization.
doi:10.1109/cvpr.2015.7298903 dblp:conf/cvpr/ZhuZZ15 fatcat:ndkdlcwavvauvhmb6o4k2eknoa