FORCE: Dataset and Method for Intuitive Physics Guided Human-object Interaction

• URL: http://arxiv.org/abs/2403.11237v1
• Date: Sun, 17 Mar 2024 14:52:05 GMT
• Title: FORCE: Dataset and Method for Intuitive Physics Guided Human-object Interaction
• Authors: Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke, Ilya Petrov, Vladimir Guzov, Helisa Dhamo, Eduardo Pérez-Pellitero, Gerard Pons-Moll,
• Abstract summary: We introduce the FORCE model, a kinematic approach for 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. Experiments also demonstrate incorporating human force facilitates learning multi-class motion.
• Score: 39.810254311528354
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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