FORCE: Dataset and Method for Intuitive Physics Guided Human-object
  Interaction
Xiaohan Zhang Bharat Lal Bhatnagar Sebastian Starke Ilya Petrov Vladimir Guzov Helisa Dhamo Eduardo Pérez-Pellitero Gerard Pons-Moll
Interactions between human and objects are influenced not only by the
object's pose and shape, but also by physical attributes such as object mass
and surface friction. They introduce important motion nuances that are
essential for diversity and realism. Despite advancements in recent
kinematics-based methods, this aspect has been overlooked. Generating nuanced
human motion presents two challenges. First, it is non-trivial to learn from
multi-modal human and object information derived from both the physical and
non-physical attributes. Second, there exists no dataset capturing nuanced
human interactions with objects of varying physical properties, hampering model
development. This work addresses the gap by introducing the FORCE model, a
kinematic approach for synthesizing diverse, nuanced human-object interactions
by modeling physical attributes. Our key insight is that human motion is
dictated by the interrelation between the force exerted by the human and the
perceived resistance. Guided by a novel intuitive physics encoding, the model
captures the interplay between human force and resistance. Experiments also
demonstrate incorporating human force facilitates learning multi-class motion.
Accompanying our model, we contribute the FORCE dataset. It features diverse,
different-styled motion through interactions with varying resistances.