4D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping
This study presents a framework for capturing human attention in the spatio-temporal domain using eye-tracking glasses. Attention mapping is a key technology for human perceptual activity analysis or Human-Robot Interaction (HRI) to support human visual cognition; however, measuring human attention in dynamic environments is challenging owing to the difficulty in localizing the subject and dealing with moving objects. To address this, we present a comprehensive framework, 4D Attention, for unified gaze mapping onto static and dynamic objects. Specifically, we estimate the glasses pose by leveraging a loose coupling of direct visual localization and Inertial Measurement Unit (IMU) values. Further, by installing reconstruction components into our framework, dynamic objects not captured in the 3D environment map are instantiated based on the input images. Finally, a scene rendering component synthesizes a first-person view with identification (ID) textures and performs direct 2D-3D gaze association. Quantitative evaluations showed the effectiveness of our framework. Additionally, we demonstrated the applications of 4D Attention through experiments in real situations.
Code (0)
등록된 구현이 없습니다.
Tasks
Visual LocalizationSimilar Papers 제목 키워드 기반
Learning Spatio-Temporal Feature Representations for Video-Based Gaze Estimation
Video-based gaze estimation methods aim to capture the inherently temporal dynamics of human eye gaze from multiple image frames. However, since models must capture both spatial and temporal relationships, performance is…
Gaze EstimationHuman Gaze Guided Attention for Surgical Activity Recognition
Modeling and automatically recognizing surgical activities are fundamental steps toward automation in surgery and play important roles in providing timely feedback to surgeons. Accurately recognizing surgical activities …
Activity RecognitionVideo UnderstandingSAGE: Synchronized Action-Gaze Recognition and Anticipation for Human Behavior Understanding
Human object interaction (HOI), gaze pattern, and their anticipation are intricately linked, providing valuable insights into cognitive processes, intentions, and behavior. However, most existing models handle gaze and a…
StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realistic applications such as Augmented Reality (AR) glasses. While prior streaming…
Video Question AnsweringUnderstanding Human Gaze Communication by Spatio-Temporal Graph Reasoning
This paper addresses a new problem of understanding human gaze communication in social videos from both atomic-level and event-level, which is significant for studying human social interactions. To tackle this novel and …
DecoderGraph Neural Network