Computer Animation and Virtual Worlds最新文献

In this paper, we present an approach to build a biometric system capable of identifying subjects based on gait. The experiments were carried out with a proprietary gait corpus collected from 100 subjects. In the data acquisition process, we used a commercially available perception neuron body suit equipped with motion sensors and dedicated to entertainment in the VR domain. Classification was performed using two variants of the CNN architecture and evaluated using cross-day validation. A novelty in the presented approach was the exploration of research areas related to the usage of synthetically generated samples. Experiments were conducted for two types of preprocessing—a low-pass filtering of the signals using a 3rd- or 1st-order Butterworth filter. For the first variant, the synthetic samples generated by the long short-term memory-mixture density network (LSTM-MDN) model allowed us to increase the F1-score from 0.928 to 0.966. Meanwhile, in the second case from 0.970 to 0.978 F1-score.

The spectrum-based method, usually referred to as the Fourier approach, is considered to be an efficient solution for synthesizing and visualizing realistic ocean waves, and is widely used in films and video games. It is based on a spectra function describing the distribution relationship between the wave energies and various individual waves of different frequencies. However, the spectrum method is based on the assumption that the sea surface is homogeneous everywhere, and cannot reproduce the various inhomogeneous real-world sea conditions for maritime simulators. This paper proposes a new model for ocean wave simulation which combines the spectral methods and wave-particle theory to result in a flexible and physically accurate model for ocean wave simulation. In the proposed model, input environmental parameters can dynamically affect the shape of the spectrum, then control the properties of the wave particles by the spectral function, thus achieving local control of waves instead of global control. Through experiments, we verify that the proposed model can capture the spatially varying characteristics and reproduce real sea state. Experimental results also show that our model has considerable physical accuracy and can achieve good compliance with the statistical characteristics of actual sea.

Virtual environments in education offer numerous benefits, as shown by the growing volume of scientific research. However, understanding the effectiveness of VR applications, and evaluating study outcomes is imperative. To explore this further, a Systematic Literature Review was conducted, revealing various methods for evaluating the effectiveness of VR applications; however, questionnaires emerged as the most used approach. Outcome analysis suggests that traditional teaching methods can be as effective as VR, though this technology shows potential in stimulating cognitive and motor skills, with positive results in motivation and engagement. Cognitive load generated by information in Virtual Environment and Cybersickness are common issues, and long-term impact and information on VR software development remain almost underexplored. These findings highlight the need for rigorous validation of VR in education, particularly to establish its long-term benefits and usability.

VizDoom is a flexible platform for researching reinforcement learning (RL) within the Doom game environment. This research article analyzes the effectiveness of the proximal policy optimization (PPO) algorithm in the VizDoom Deadly Corridor scenario. The PPO algorithm has not been adequately assessed before in a first-person shooter-based research environment, specifically VizDoom. Thus, this article applied reward shaping and curriculum learning techniques to improve the algorithm's performance in complex and challenging scenarios of the first-person shooter game Doom. The goal is to analyze and evaluate the effectiveness of the PPO algorithm successfully in the scenario of the three-dimensional VizDoom environment. The agent has a record score up to 734 on the first hard level, 1576 on the second hard level, 1920 on the third hard level, 2280 on the fourth hard level, and 1605 on the fifth hard level which is the highest difficult level of the scenario. The results are compared to provide valuable insights for researchers in optimizing reinforcement learning agents in games. The study also discusses the potential of the Doom game for research in artificial intelligence. The results of this study can be used to enhance the performance of reinforcement learning algorithms in game-based environments.

While numerous motion capture tools have emerged, scarcer are solutions tailored for code-free utilization environments. An intuitive no-code interface promises greater accessibility for diverse user groups. Guided by this premise, the myKLA2 toolkit incorporates integrated decoder, editor, and player components tailored for code-free motion development workflows. Leveraging cyber-physical system principles, the software enables users to customize digital avatars by selecting from a large repository of sharable 3D VRM models. Additionally, McCabe's cyclomatic complexity analysis facilitated iterative refactoring to optimize system capabilities and toolset performance. Advanced joint tracking and motion capture algorithms enable precise avatar animation mirroring user movements in real-time. A human-readable JSON format facilitates configurable behavior encoding and exchange. This research demonstrates the system's potential as a versatile no-code toolkit for rapid development and design across healthcare, education, and other domains. By automating core tasks like motion tracking configuration, sensor calibration, and basic quantitative assessments, this tool promises to streamline therapist workflows in telerehabilitation.

In this paper, we present an Augmented Reality (AR)-based application combined with a robotic system for percutaneous renal puncture navigation interaction and demonstrate its technical feasibility. Our system provides an intuitive interaction scheme between the surgeon and the robot without the need for traditional external input devices, and applies an image-target-based 3D registration scheme to transform the coordinate system between Hololens2 and the robot without using additional tracking devices. Users can visualize the abdominal puncture phantom and obtain 3D depth information of the lesion site by wearing Hololens2 and control the robot directly using buttons or gestures. To investigate the accuracy and feasibility of the proposed interaction scheme, six subjects were recruited to complete 3D registration alignment accuracy experiments, and puncture positioning accuracy experiments using ultrasound unaided navigation, AR unaided navigation and AR robotic navigation. The results showed that the average alignment error of 3D registration was 3.61 ± 1.05 mm. The average positioning errors of ultrasound freehand navigation, AR freehand navigation and AR robotic navigation were 7.67 ± 2.00 mm, 6.13 ± 1.07 mm and 5.52 ± 0.37 mm, respectively; the average puncture times were 34.86 ± 1.67 s, 22.40 ± 2.07 s, and 29.41 ± 1.37 s.

Addressing the challenges of low feature extraction dimensions and insufficient distinct information for gesture differentiation for smart home appliances, this article proposed an innovative gesture recognition algorithm, integrating fractional Fourier transform (FrFT) with relevance vector machine (RVM). The process involves using FrFT to transform raw gesture data into the fractional domain, thereby expanding the dimensions of information extraction. Subsequently, high-dimensional feature vectors are created from fractional domain, and RVM classifiers are employed for joint optimization of feature selection and classification decision functions, achieving optimal classification performance. A dataset was constructed using five different types of gestures recorded on the TI millimeter-wave radar platform to validate the effectiveness of this method. The experimental results demonstrate that the RVM selected the optimal FrFT order of 0.6, with the best feature set comprising fractional spectral entropy, peak factor, and second-order central moment. Recognition rates for each gesture exceeded 96.2%, with an average rate of 98.5%. This performance surpasses three comparative methods in both recognition accuracy and real-time processing, indicating high potential for future applications.

Pan-tilt-zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large-scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding environment. To achieve this goal, some research projects camera videos into three-dimensional (3D) models or panoramas and allows operators to establish spatial relationships. However, these works face several challenges in terms of real-time processing, localization accuracy, and realistic reference. To address this problem, a visual expansion and real-time calibration for PTZ cameras assisted by panoramic models is proposed. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, meeting the needs of real-time processing by establishing a PTZ camera motion estimation model and achieving high-precision alignment between PTZ images and panoramic models using only two feature point pairs. Our methods were validated using both the public and our Scene dataset. The experimental results indicate that our method outperforms other state-of-the-art methods in terms of real-time processing, accuracy, and robustness.

Popular in fantasy films, games, and extended reality, anthropomorphic animals often rely on animator creativity and real animal observation for behavior visualization. This artistic approach captures emotional traits but lacks uncovering diverse, unanticipated behaviors beyond creators' concepts. To enrich character design, this study employs reinforcement learning (RL) agent simulation to explore the autonomous behavior and unexpected responses of the nine-tailed Fox Sister from Korean folklore. As a method, the agent, with a physics-based controller and skeletal joints, uses hybrid action control to transition between bipedal and quadrupedal actions based on the environment. In result, RL character frequently exhibits behavioral shifts, including unexpected actions in response to training steps and terrain complexities like slopes and hurdles, distinguishing them from animation-based finite-state machines. Additionally, this study validates impacts of RL character on character design creativity. To investigate such unknown impacts, this study conducts a comparative pilot study that recruits five character designers under use and nonuse scenario of RL character. Analysis indicates that RL character promotes creativity of character design, conceptualization, and development of scenario and character's attribute. This study highlights RL's potential for visualizing diverse inspirational behaviors of folkloric creatures by simulating interactions between body structure, motion, and environment.

Autism spectrum disorder (ASD) is a neurological condition that affects an individual's mental development. This research work implements a multimodality input-based virtual reality (VR)-enabled attention prediction approach in gaming for children with autism. Initially, the multimodal inputs such as face image, electroencephalogram (EEG) signal, and data are individually processed by both the preprocessing and feature extraction procedures. Subsequently, a hybrid classification model with classifiers such as improved deep convolutional neural network (IDCNN) and long short term memory (LSTM) is utilized in expression detection by concatenating the resultant features obtained from the feature extraction procedure. Here, the conventional deep convolutional neural network (DCNN) approach is improved by a novel block-knowledge-based processing with a proposed sine-hinge loss function. Finally, an improved weighted mutual information process is employed in attention prediction. Moreover, this proposed attention prediction model is analyzed by simulation and experimental analyses. The effectiveness of the proposed model is significantly proved by the experimental results obtained from various analyses.