With the full representation of the object shape and joint states, we can address several tasks including category-level object pose estimation and the articulated object retrieval.
To ensure the visual style consistency between the foreground and the background, in this paper, we treat image harmonization as a style transfer problem.
This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world.
Previous methods edit an input image under the guidance of a discrete emotion label or absolute condition (e. g., facial action units) to possess the desired expression.
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.
Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision.