  We propose a low cost and effective way to combine a free simulation software
and free CAD models for modeling human-object interaction in order to improve
human & object segmentation. It is intended for research scenarios related to
safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial
domain. The task of human and object modeling has been used for detecting
activity, and for inferring and predicting actions, different from those works,
we do human and object modeling in order to learn interactions in RGB-D data
for improving segmentation. For this purpose, we define a novel density
function to model a three dimensional (3D) scene in a virtual environment
(VREP). This density function takes into account various possible
configurations of human-object and object-object relationships and interactions
governed by their affordances. Using this function, we synthesize a large,
realistic and highly varied synthetic RGB-D dataset that we use for training.
We train a random forest classifier, and the pixelwise predictions obtained is
integrated as a unary term in a pairwise conditional random fields (CRF). Our
evaluation shows that modeling these interactions improves segmentation
performance by ~7\% in mean average precision and recall over state-of-the-art
methods that ignore these interactions in real-world data. Our approach is
computationally efficient, robust and can run real-time on consumer hardware.
