AR Hero: Generating Interactive Augmented Reality Guitar Tutorials

Skreinig, Lucchas Ribeiro; Stănescu, Ana Maria Alexandra; Mori, Shohei; Heyen, Frank; Mohr, Peter; Sedlmair, Michael; Schmalstieg, Dieter; Kalkofen, Denis

doi:10.1109/vrw55335.2022.00086

Cited by 6 publications

(7 citation statements)

References 16 publications

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“…The perception of hand shape, movement, and hand joint points has become vital in achieving user experiences in various technical disciplines and platforms [1,[32][33][34][35]. For instance, augmented reality apps enable digital material, which has been increasingly popular in recent years, to be presented in conjunction with the actual environment [36,37]. Aside from that, the perception of hand movements is used in areas such as sign language perception and digital content presentation [38][39][40].…”

Section: Data Collection and Descriptionmentioning

confidence: 99%

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Salturk,

Kahraman

2024

Neural Comput & Applic

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The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

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Section: Data Collection and Descriptionmentioning

confidence: 99%

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Salturk,

Kahraman

2024

Neural Comput & Applic

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“…Online Illumination Learning for Interactive Global Illumination in Augmented Reality. [2] To make the augmented objects to become visually coherent with real objects.…”

Section: IImentioning

confidence: 99%

“…2) Technical improvements: The literature also emphasizes the impact of technical improvements in driving the adoption of AR in education. Research studies [2], [5], and [6] exhibit novel AR and platforms developed to support diverse educational activities, including virtual lessons, simulations, and interactive learning modules. These improvements underline the need of integrating AR technology into educational settings to address the increasing demands of students and educators.…”

Section: Analysis and Synthesis A Analysis 1)mentioning

confidence: 99%

ARcademy: Augmented Reality Adventures in Education

Khada,

Kambli,

Kadam

et al. 2024

IJRASET

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Augmented reality (AR) technology holds enormous promise for transforming education by enabling interactive and immersive learning experiences. This research article provides an AR application built to highlight the different possibilities of AR in educational contexts. The program incorporates different interactive aspects, including a virtual dragon, as a showcase of the adaptability and potential of AR for boosting learning experiences. Users can connect with the virtual dragon and explore its interactions within the real-world setting, showing the seamless integration of digital material into physical spaces. Through a user-centered design approach, our team has built an easy and engaging AR experience geared at promoting curiosity, creativity, and critical thinking skills among learners. By exploiting the unique affordances of AR technology, educators can develop dynamic learning environments that cater to varied learning styles and interests. This study assesses the effectiveness of the AR application in encouraging active learning and boosting student engagement across diverse educational environments. It also investigates practical aspects such as usability, accessibility, and scalability to influence future innovations and implementations of AR-based teaching systems. By giving a real example of AR integration in education, our research contributes to the continuing discourse on the transformative potential of AR technology. It emphasizes the necessity of developing creative techniques to harness AR for interactive and personalized learning experiences in the digital age.

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“…Combined with more explicit depth geometry prior, we could expect improved rendering quality with additional costs [10,21,28,61]. Since multi-layer scene representation is a general 3D scene representation empowered by conventional and thus lightweight rendering pipeline, its applications in VR/AR range from 3D scene photos [13] and videos [5] for remote user assistance [45] to a dense scene reconstruction for neareye displays [12]. Compared to the NeRF implicit representation [34], the multi-layer scene representation is explicit (cf.…”

Section: Multi-layer Scene Representationmentioning

confidence: 99%

Multi-Layer Scene Representation from Composed Focal Stacks

Ishikawa,

Saito,

Kalkofen

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: We propose representing scenes as composed focal stacks, which we compute from registered images, for view synthesis. Our approach enables diminishing local artifacts such as motion blur and ISO noise, to improve multi-layer scene representations. (a) Our approach allows the user to randomly capture photos in a continuous motion. (b) From the captured images, we compose a synthetic focal stack, from which we derive a multi-layer scene representation using a CNN. (c) The outcome of the network is a multi-layer scene representation, which enables continuous viewpoint changes. (d) Our approach supports several applications, including 6 degrees of freedom wide field of view scene representation, which enables photo-realistic VR applications (left), scene representations from underexposed images (right), and multi-layer image generation from captured focal stacks (top).

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AR Hero: Generating Interactive Augmented Reality Guitar Tutorials

Cited by 6 publications

References 16 publications

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

ARcademy: Augmented Reality Adventures in Education

Multi-Layer Scene Representation from Composed Focal Stacks

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