3D Virtual-Reality Interaction System

Huang, Xinyu; Tsai, Meng-Shiun; Huang, Ching-Chun

doi:10.1109/icce-tw46550.2019.8991800

Cited by 9 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[105][106][107][108][109] Deep learning techniques have also been developed to extract 2D or 3D human posture information from camera data, and semantic segmentation algorithms can discern between pixels of virtual and real objects. [110][111][112][113][114][115][116][117][118] Brain-Computer Interface (BCI) technology, 119,120 which converts brain impulses into commands understood by computing devices, can also be used to merge the virtual and physical worlds. Research in BCI based on AI technology is ongoing.…”

Section: Visualization Systemmentioning

confidence: 99%

“…Techniques like Simultaneous Localization and Mapping (SLAM) can be used to learn the 3D structure and motion of an unfamiliar environment, while eye‐tracking can enhance user microinteractions 105–109 . Deep learning techniques have also been developed to extract 2D or 3D human posture information from camera data, and semantic segmentation algorithms can discern between pixels of virtual and real objects 110–118 . Brain‐Computer Interface (BCI) technology, 119,120 which converts brain impulses into commands understood by computing devices, can also be used to merge the virtual and physical worlds.…”

Section: Cutting‐edge Technologies For Enabling Metaversementioning

confidence: 99%

See 1 more Smart Citation

The realm of metaverse: A survey

Venugopal

Subramanian

Peatchimuthu

2023

Computer Animation & Virtual

View full text Add to dashboard Cite

The next step of digital development is the metaverse, which has the potential to drastically alter how people use technology and expand the range of services available beyond conventional systems that can be accessed online. As the efficiency, performance, and quality of service access reach their peak levels, the focus has shifted to the user experience. Due to this, there is an increasing demand for more involved and thorough customer service, and service providers are willing to increase their present standards. Consumers are genuinely asking for tactile and immersive elements in their digital interfaces, but these features can only be made possible by the metaverse's potentially futuristic subfields of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR). However, the metaverse may not be widely used due to significant security and privacy issues either from underlying technology or produced by the new digital environment. A variety of fundamental problems, such as scalability and interoperability, can arise in terms of ensuring security for the metaverse because of the metaverse's inherent properties, such as immersive realism, sustainability, and heterogeneity. In this survey, we propose a hypothetical meta‐stack framework to understand the various components in the realm of metaverse and then provide wide‐ranging insights on the most recent development in metaverse realm in the context of cutting‐edge technologies, security vulnerabilities and preventive measures specific to the metaverse and the research challenges pertaining to metaverse.

show abstract

Section: Visualization Systemmentioning

confidence: 99%

Section: Cutting‐edge Technologies For Enabling Metaversementioning

confidence: 99%

The realm of metaverse: A survey

Venugopal

Subramanian

Peatchimuthu

2023

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…The situation is analyzed using mathematical models of the convex hull. To form a convex polygon, the gesture keypoints (0,1,2, 3,6,10,14,19,18,17) are connected by the convex hull algorithm. The convex hull of the detected hand is depicted by the blue line in Fig.…”

Section: Gesture Recognition Using the Convex Hull Algorithm And Hand...mentioning

confidence: 99%

“…Particularly under the background of epidemic situation, human-computer interaction has become increasingly important. In addition to human-computer interaction (HCI), hand pose estimation and gesture recognition [1][2][3] have applications in virtual real-ity (VR) and augmented reality (AR). During gesture recognition, the finger curvature characteristic is utilized based on the keypoint coordinates obtained by hand pose estimation.…”

Section: Introductionmentioning

confidence: 99%

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

Zhang

Zhou

Tao

et al. 2023

IFS

View full text Add to dashboard Cite

The modern world contains a significant number of applications based on computer vision, in which human-computer interaction plays a crucial role, pose estimation of the hand is a crucial approach in the field of human-computer interaction. However, previous approaches suffer from the inability to accurately measure position in real-world scenes, difficulty in obtaining targets of different sizes, the structure of complex network, and the lack of applications. In recent years, deep learning techniques have produced state-of-the-art outcomes but there are still challenges that need to be overcome to fully exploit this technology. In this research, a fish skeleton CNN (FS-HandNet) is proposed for hand posture estimation from a monocular RGB image. To obtain hand pose information, a fish skeleton network structure is used for the first time. Particularly, bidirectional pyramid structures (BiPS) can effectively reduce the loss of feature information during downsampling and can be used to extract features from targets of different sizes. It is more effective at solving problems of different sizes. Then a distribution-aware coordinate representation is employed to adjust the position information of the hand, and finally, a convex hull algorithm and hand pose information are applied to recognize multiple gestures. Extensive studies on three publicly available hand position benchmarks demonstrate that our method performs nearly as well as the state-of-the-art in hand pose estimation. Additionally, we have implemented hand pose estimation for the application of gesture recognition.

show abstract

“…The gesture is among the most commonly used expressions by humans, and accurate 3D hand pose estimation has already become a key technology in the fields of Human-Computer Interaction (HCI) and Virtual Reality (VR) [ 1 , 2 , 3 , 4 , 5 ]. It can help humans communicate with machines in a more natural way.…”

Section: Introductionmentioning

confidence: 99%

3D Hand Pose Estimation Based on Five-Layer Ensemble CNN

Fan

Hong

2021

Sensors

View full text Add to dashboard Cite

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods.

show abstract

3D Virtual-Reality Interaction System

Cited by 9 publications

References 4 publications

The realm of metaverse: A survey

The realm of metaverse: A survey

Hand pose estimation based on fish skeleton CNN: application in gesture recognition

3D Hand Pose Estimation Based on Five-Layer Ensemble CNN

Contact Info

Product

Resources

About