  Affordance grounding aims to locate objects' "action possibilities" regions,
which is an essential step toward embodied intelligence. Due to the diversity
of interactive affordance, the uniqueness of different individuals leads to
diverse interactions, which makes it difficult to establish an explicit link
between object parts and affordance labels. Human has the ability that
transforms the various exocentric interactions into invariant egocentric
affordance to counter the impact of interactive diversity. To empower an agent
with such ability, this paper proposes a task of affordance grounding from
exocentric view, i.e., given exocentric human-object interaction and egocentric
object images, learning the affordance knowledge of the object and transferring
it to the egocentric image using only the affordance label as supervision.
However, there is some "interaction bias" between personas, mainly regarding
different regions and different views. To this end, we devise a cross-view
affordance knowledge transfer framework that extracts affordance-specific
features from exocentric interactions and transfers them to the egocentric
view. Specifically, the perception of affordance regions is enhanced by
preserving affordance co-relations. In addition, an affordance grounding
dataset named AGD20K is constructed by collecting and labeling over 20K images
from $36$ affordance categories. Experimental results demonstrate that our
method outperforms the representative models regarding objective metrics and
visual quality. Code is released at
https://github.com/lhc1224/Cross-view-affordance-grounding.
