12 accepted papers
Full-body motion estimation from sparse VR observations is an inherently under-constrained problem, with only three 6-DoF trackers (HMD and controllers) available to infer a full skeletal pose. To address this ambiguity, we introduce a probabilistic framework that models joint orientations as distri
Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vision-based methods due to occlusions and constrained capture volumes. Inertial measurement units (IMUs) offer a compelling alternative without line-
Millimeter-wave radar offers a privacy-preserving and environment-robust alternative to vision-based sensing, enabling human motion analysis in challenging conditions such as low light, occlusions, rain, or smoke. However, its sparse point clouds pose significant challenges for semantic understandin
Accurate 3D reconstruction is crucial for AR and VR applications. Compared with traditional pinhole camera-based methods, 360° image-based reconstruction can achieve higher precision with fewer input images, making it especially effective in low-texture environments. However, the severe distortion r
Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (…
With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, whil
Millimeter-wave (mmWave) radar offers robust sensing capabilities in diverse environments, making it a highly promising solution for human body reconstruction due to its privacy-friendly and non-intrusive nature. However, the significant sparsity of mm Wave point clouds limits the estimation accurac
This study presents a novel approach to enhance the accuracy and adaptability of pedestrian positioning by fusing data from multiple Inertial Measurement Units (IMUs) attached to the human body. Leveraging the temporal and spatial richness of IMU data, our proposed multi-node sensors fusion strategy…
This paper introduces a novel human pose estimation approach using sparse inertial sensors addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and mode…
In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant…
Human body reconstruction with Millimeter Wave (mmWave) radar point clouds has gained significant interest due to its ability to work in adverse environments and its capacity to mitigate privacy concerns associated with traditional camera-based solutions. Despite pioneering efforts in this field, tw…
Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance field…