FusionKit

Rietzler, Michael; Geiselhart, Florian; Thomas, Janek; Rukzio, Enrico

doi:10.1145/2933242.2933263

Cited by 12 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kinects V2 sensors can provide information on joints and movement of people in the field of view, that our system already allows to display. Advanced tracking solutions exists (markerless body tracking, marker-based object tracking) using multiple Kinect v2 devices, such as the one developed by Rietzler et al [16] who designed an open source software providing low-cost tracking system. In their experiment on skeletonbased tracking, using a statistical nonlinear boosting model that allows predicting the magnitude of tracking errors with accuracy, they were able to reduce drastically the error compared to a single Kinect v2.…”

Section: Discussionmentioning

confidence: 99%

Real-time 360 Body Scanning System for Virtual Reality Research Applications

Albert¹,

Lance²,

Dubessy³

et al. 2019

Proceedings of 3DBODY.TECH 2019 - 10th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

View full text Add to dashboard Cite

Here, we present a low-cost solution to perform the online and realistic representation of users using an array of depth cameras. The system is composed of a cluster of 10 Microsoft Kinect 2 cameras, each one associated to a compact NUC PC to stream live depth & color images to a master PC which reconstructs live the point cloud of the scene and can in particular show the body of users standing in the capture area. A custom geometric calibration procedure allows accurate reconstruction of the different 3D data streams. Despite the inherent limitations of depth cameras, in particular sensor noise, the system provides a convincing representation of the user's body, is not limited by changes in clothing (also during immersion), can capture complex poses and even interactions between two persons or with physical objects. The advantage of using depth cameras over conventional cameras is that little processing is required for dynamic reconstruction of unknown shapes, thus allowing true interactive applications. The resulting live 3D model can be inserted in any virtual environment (e.g. Unity 3D software integration plugin), and can be subject to all usual 3D manipulation and transformations.

show abstract

Section: Discussionmentioning

confidence: 99%

Real-time 360 Body Scanning System for Virtual Reality Research Applications

Albert¹,

Lance²,

Dubessy³

et al. 2019

Proceedings of 3DBODY.TECH 2019 - 10th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

View full text Add to dashboard Cite

show abstract

“…For tracking the movements, we used the FusionKit [Rietzler et al 2016], a software designed for the fusion of multiple Kinect V2 sensors to enlarge the tracking space and optimize the accuracy compared to a single Kinect setup. The fused skeletal data was streamed via UDP to a mobile Unity3D application.…”

Section: Setupmentioning

confidence: 99%

Demo of the Matrix Has You: Realizing Slow Motion in Full-Body Virtual Reality

Rietzler¹,

Geiselhart²,

Brich³

et al. 2018

2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

Self Cite

View full text Add to dashboard Cite

While we perceive time as a constant factor in the real world, it can be manipulated in media. Being quite easy for linear media, this is used for various aspects of storytelling e.g., by applying slow motion in movies or TV. Interactive media like VR however poses additional challenges, because user interaction speed is independent from media speed. While it is still possible to change the speed of the environment, for interaction it is also necessary to deal with the emerging speed mismatch, e.g., by slowing down visual feedback of user movements. In this paper, we explore the possibility of such manipulations of visual cues, with the goal of enabling the use of slow motion also in immersive interactive media like VR. We conducted a user study to investigate the impact of limiting angular velocity of a virtual character in first person view in VR. Our findings show that it is possible to use slow motion in VR while maintaining the same levels of presence, enjoyment and susceptibility to motion sickness, while users adjust to the maximum speed quickly. Moreover, our results also show an impact of slowing down user movements on their time estimations.

show abstract

“…Human motion tracking is widely used in the animation industry [19,43], computer games [30,47], human-computer interaction [3] and medical rehabilitation applications [21,24,28,41]. Currently, optical solutions and inertial measurement units (IMUs) are the most popular approaches to tracking human motion that have mature applications [18,31]. Between them, optical solutions suffer from bad environmental conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Chen

Jiang

Zhan

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

Human motion tracking is a fundamental building block for various applications including computer animation, human-computer interaction, healthcare, etc. To reduce the burden of wearing multiple sensors, human motion prediction from sparse sensor inputs has become a hot topic in human motion tracking. However, such predictions are non-trivial as i) the widely adopted data-driven approaches can easily collapse to average poses. ii) the predicted motions contain unnatural jitters. In this work, we address the aforementioned issues by proposing a novel framework which can accurately predict the human joint moving angles from the signals of only four flexible sensors, thereby achieving the tracking of human joints in multi-degrees of freedom. Specifically, we mitigate the collapse to average poses by implementing the model with a Bi-LSTM neural network that makes full use of short-time sequence information; we reduce jitters by adding a median pooling layer to the network, which smooths consecutive motions. Although being bio-compatible and ideal for improving the wearing experience, the flexible sensors are prone to aging which increases prediction errors. Observing that the aging of flexible sensors usually results in drifts of their resistance ranges, we further propose a novel dynamic calibration technique to rescale sensor ranges, which further improves the prediction accuracy. Experimental results show that our method achieves a low and stable tracking error of 4.51 degrees across different motion types with only four sensors.

show abstract

FusionKit

Cited by 12 publications

References 18 publications

Real-time 360 Body Scanning System for Virtual Reality Research Applications

Real-time 360 Body Scanning System for Virtual Reality Research Applications

Demo of the Matrix Has You: Realizing Slow Motion in Full-Body Virtual Reality

Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Contact Info

Product

Resources

About