EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views

TL;DR

EgoChoir is a novel method that infers 3D human-object interaction regions from egocentric videos by integrating appearance, head motion, and object structure, improving understanding of interactions for AR/VR and AI applications.

Contribution

It introduces EgoChoir, a framework that models object affordance and human contact from egocentric views by combining visual cues and head motion, addressing limitations of previous exocentric methods.

Findings

EgoChoir outperforms existing methods in capturing 3D interaction regions.

The approach effectively models object affordance and human contact.

Experiments validate the method's superiority on Ego-Exo4D and GIMO datasets.

Abstract

Understanding egocentric human-object interaction (HOI) is a fundamental aspect of human-centric perception, facilitating applications like AR/VR and embodied AI. For the egocentric HOI, in addition to perceiving semantics e.g., ''what'' interaction is occurring, capturing ''where'' the interaction specifically manifests in 3D space is also crucial, which links the perception and operation. Existing methods primarily leverage observations of HOI to capture interaction regions from an exocentric view. However, incomplete observations of interacting parties in the egocentric view introduce ambiguity between visual observations and interaction contents, impairing their efficacy. From the egocentric view, humans integrate the visual cortex, cerebellum, and brain to internalize their intentions and interaction concepts of objects, allowing for the pre-formulation of interactions and making behaviors even when interaction regions are out of sight. In light of this, we propose harmonizing the visual appearance, head motion, and 3D object to excavate the object interaction concept and subject intention, jointly inferring 3D human contact and object affordance from egocentric videos. To achieve this, we present EgoChoir, which links object structures with interaction contexts inherent in appearance and head motion to reveal object affordance, further utilizing it to model human contact. Additionally, a gradient modulation is employed to adopt appropriate clues for capturing interaction regions across various egocentric scenarios. Moreover, 3D contact and affordance are annotated for egocentric videos collected from Ego-Exo4D and GIMO to support the task. Extensive experiments on them demonstrate the effectiveness and superiority of EgoChoir.