ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions
(2401.10232)Abstract
To enable machines to learn how humans interact with the physical world in our daily activities, it is crucial to provide rich data that encompasses the 3D motion of humans as well as the motion of objects in a learnable 3D representation. Ideally, this data should be collected in a natural setup, capturing the authentic dynamic 3D signals during human-object interactions. To address this challenge, we introduce the ParaHome system, designed to capture and parameterize dynamic 3D movements of humans and objects within a common home environment. Our system consists of a multi-view setup with 70 synchronized RGB cameras, as well as wearable motion capture devices equipped with an IMU-based body suit and hand motion capture gloves. By leveraging the ParaHome system, we collect a novel large-scale dataset of human-object interaction. Notably, our dataset offers key advancement over existing datasets in three main aspects: (1) capturing 3D body and dexterous hand manipulation motion alongside 3D object movement within a contextual home environment during natural activities; (2) encompassing human interaction with multiple objects in various episodic scenarios with corresponding descriptions in texts; (3) including articulated objects with multiple parts expressed with parameterized articulations. Building upon our dataset, we introduce new research tasks aimed at building a generative model for learning and synthesizing human-object interactions in a real-world room setting.
Overview
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ParaHome is a breakthrough system for parameterizing 3D human-object interactions in home settings using 70 synchronized RGB cameras and wearable motion capture devices.
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The dataset to be released is comprehensive, capturing full-body and hand movements, object and articulated parts movements in a real-world room.
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The system provides a parameterized 3D space for better understanding and predicting complex human-object interactions.
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Probabilistic models are suggested for inferring plausible interactions from the collected data.
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The system aims to enhance research in robotics and virtual/augmented reality by understanding the causal and spatiotemporal aspects of HOI.
Overview of ParaHome
The research presented introduces a breakthrough system, ParaHome, for capturing and parameterizing 3D interactions between humans and objects within a home setting. The core of this system is a specialized setup combining 70 synchronized RGB cameras and wearable motion capture devices, which track both the gross movements of the body across a room and the fine dexterous movements of the hands.
Data Collection and Unique Features
ParaHome system's data collection has been extensive, with a particular focus on the authenticity and variety of human-object interactions. The dataset, which will be publicly available, stands out for its comprehensiveness by capturing 3D full-body and hand movements, movements of various objects, and their articulated parts within a real-world room setting. A summary of the key advancements offered by the dataset includes:
- Integration of dexterous human actions and object movements in a shared parameterized space.
- Capture of human interaction with multiple objects in an array of naturally occurring activities.
- Inclusion of objects with articulated parts, such as laptops and kitchen drawers, providing a new layer of interaction complexity.
Modeling Human-Object Interactions
ParaHome's goal extends beyond tracking to understanding and predicting human-object interactions (HOI). To facilitate this, the system and associated study introduce a parameterized 3D space with human pose parameters and object parameters to capture the nuanced dynamics of these interactions. Moreover, the paper suggests probabilistic modeling approaches to predict or infer plausible configurations and dynamics from the data.
Implications and Future Directions
The innovative ParaHome system enables the deep study of the causal and spatiotemporal relationships within human-object interactions. The resulting dataset not only provides significant improvements over existing datasets but also paves the way for future research in generative modeling of HOI. The researchers recognize the system's current limitations, such as the inability to use RGB videos for training models due to markers on suits, and plan enhancements, including more diverse environments and objects. This endeavor reflects the ongoing research commitment to understanding complex interactions in home environments that are crucial for advancements in robotics as well as virtual and augmented reality simulations.
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