Eye tracking is crucial for human-computer interaction in different domains. Conventional
cameras encounter challenges such as power consumption and image quality during …

This paper addresses the problem of cross-modal object tracking from RGB videos and
event data. Rather than constructing a complex cross-modal fusion network, we explore the …

Tracking objects in 3D space and predicting their 6DoF pose is an essential task in
computer vision. State-of-the-art approaches often rely on object texture to tackle this …

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current
methods still cannot reliably estimate moving humans in global coordinates, which is critical …

We consider the task of 3D pose estimation and trackingof multiple people seen in an
arbitrary number of camerafeeds. We propose TesseTrack, a novel top-down approachthat …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

Current 3D single object tracking approaches track the target based on a feature
comparison between the target template and the search area. However, due to the common …

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This
representation mimics the well-studied image-based 2D bounding-box detection but comes …

Following the tracking-by-attention paradigm, this letter introduces an object-centric,
transformer-based framework for tracking in 3D. Traditional model-based tracking …

Without temporal averaging, such as rate codes, it remains challenging to train spiking
neural networks for temporal regression tasks. In this work, we present a novel method to …