  Humans interact with an object in many different ways by making contact at
different locations, creating a highly complex motion space that can be
difficult to learn, particularly when synthesizing such human interactions in a
controllable manner. Existing works on synthesizing human scene interaction
focus on the high-level control of action but do not consider the fine-grained
control of motion. In this work, we study the problem of synthesizing scene
interactions conditioned on different contact positions on the object. As a
testbed to investigate this new problem, we focus on human-chair interaction as
one of the most common actions which exhibit large variability in terms of
contacts. We propose a novel synthesis framework COUCH that plans ahead the
motion by predicting contact-aware control signals of the hands, which are then
used to synthesize contact-conditioned interactions. Furthermore, we contribute
a large human-chair interaction dataset with clean annotations, the COUCH
Dataset. Our method shows significant quantitative and qualitative improvements
over existing methods for human-object interactions. More importantly, our
method enables control of the motion through user-specified or automatically
predicted contacts.
