FORCE: Physics-aware Human-object Interaction
Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke, Ilya Petrov,, Vladimir Guzov, Helisa Dhamo, Eduardo P\'erez-Pellitero, Gerard Pons-Moll

TL;DR
This paper introduces FORCE, a physics-aware model for synthesizing nuanced human-object interactions by modeling physical attributes like mass and friction, supported by a new dataset capturing diverse interactions.
Contribution
The paper presents a novel physics-guided approach and dataset for generating and studying nuanced human-object interactions considering physical properties.
Findings
FORCE effectively models the interplay between human force and object resistance.
Incorporating physical attributes improves the diversity and realism of generated interactions.
The dataset enables better training and evaluation of physics-aware interaction models.
Abstract
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 human-object interaction 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, an approach for synthesizing diverse, nuanced human-object interactions by modeling physical attributes. Our key insight is that human motion is…
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Taxonomy
TopicsTime Series Analysis and Forecasting
