Real-time Character Research Articles

Abuzaharen's Challenge: Building Sustainability Competencies through Science-Fiction Narratives and Game-based Learning

This paper presents a case study of the evolution of science-fiction themed game-based learning across two Masters units in a higher education course on sustainable development. The authors created three sustainability games to be played by classes in live workshop settings – a card matching game about urban challenges, a hackathon-style sustainability innovation challenge, and a systemic change analysis game - to build students’ capacity to think about systems, sustainability values, and strategies to advance positive change. The story of a bumbling, but well-intentioned, alien protagonist, Abuzaharen, serves as a dynamic backdrop to the games, which see students ‘transported’ to an alternative reality to complete time-bound challenges in a rigorous yet engaging manner in order to find their way ‘home’ to Earth. This paper documents how each game’s narrative, storytelling, design, and play evolved through multiple iterations over four years, drawing on critical reflection, evidence of engagement and student feedback. The paper also reflects on the affordances of generative artificial intelligence (GenAI) for game-based learning designers. One of the games has integrated GenAI to make Abuzaharen a real-time character in the delivery of the game, including judging student’s innovation pitches and generating live narrative content about game outcomes. The GenAI alien agent subsequently appears in ‘regular’ learning content as a course tutor. Qualitative feedback and educator observations suggest the games increased active class participation, built peer connections, fostered skills in problem-solving, promoted collaboration, and modelled creativity. The games also challenge existing narratives about negative impacts of GenAI on tertiary education by illustrating productive and creative uses of GenAI for students and educators. This paper argues that immersive, narrative-driven approaches, coupled with the strategic use of GenAI, can significantly enrich higher education for sustainability and other educational experiences by scaffolding selected competencies and applying complex concepts to real-world challenges in a light-hearted way. It concludes with practice-led insights and directions for future exploration of interest to sustainability educators, designers of interactive learning, and those experimenting with GenAI in narrative-based games.

Modeling quick autonomous response for virtual characters in safety education games

Serious games have a wide range of applications. Modeling virtual character behaviors and emotions is a challenging task in developing serious games. To generate real-time responses, behavioral and emotional models must be simple and effective. Existing studies have paid little attention to the semantic understanding of virtual characters to external stimuli and have not effectively linked perceived semantics and motivation. This paper proposes a cognitive structure for the virtual character. The structure contains multiple modules: perception, personality, motivation, behavior, and emotion. Based on psychological theory, a semantic table that connects external stimuli, motivations, behaviors, and emotions is designed for each virtual character. Perceptivity is introduced to measure the degree of perception. According to Maslow’s motivation theory, a quantitative description of motivation is given and a discriminating method is proposed to generate behaviors and emotions. A prototype of a serious game is developed to verify the validity of the proposed method. The experimental results show that the proposed method can simulate the behavior and emotion of virtual characters in real time and will enhance the immersion of serious games.

Interactive Character Control with Auto-Regressive Motion Diffusion Models

Real-time character control is an essential component for interactive experiences, with a broad range of applications, including physics simulations, video games, and virtual reality. The success of diffusion models for image synthesis has led to the use of these models for motion synthesis. However, the majority of these motion diffusion models are primarily designed for offline applications, where space-time models are used to synthesize an entire sequence of frames simultaneously with a pre-specified length. To enable real-time motion synthesis with diffusion model that allows time-varying controls, we propose A-MDM (Auto-regressive Motion Diffusion Model). Our conditional diffusion model takes an initial pose as input, and auto-regressively generates successive motion frames conditioned on the previous frame. Despite its streamlined network architecture, which uses simple MLPs, our framework is capable of generating diverse, long-horizon, and high-fidelity motion sequences. Furthermore, we introduce a suite of techniques for incorporating interactive controls into A-MDM, such as task-oriented sampling, in-painting, and hierarchical reinforcement learning (See Figure 1). These techniques enable a pre-trained A-MDM to be efficiently adapted for a variety of new downstream tasks. We conduct a comprehensive suite of experiments to demonstrate the effectiveness of A-MDM, and compare its performance against state-of-the-art auto-regressive methods.

Overview of the application of artificial intelligence in computer animation

With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.

Review on Isolated Urdu Character Recognition: Offline Handwritten Approach

Abstract: This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic, Persian, and Sindhi, proposing various techniques like feature extraction, deep learning, and machine learning to enhance character recognition technology. The survey highlights specific studies with high accuracy and discusses recognition systems for Arabic characters as well.

Novel approach of Predicting Human Sentiment using Deep Learning

Due to its interactive and real-time character, gathering public opinion through the analysis of massive social data has garnered considerable attention. Recent research have used sentiment analysis and social media to do this in order to follow major events by monitoring people's behavior. In this article, we provide a flexible approach to sentiment analysis that instantly pulls user opinions from social media postings and evaluates them. As time passed, an increasing number of people shared their opinions on social media. More individuals can now communicate with one another as a result. Along with these advantages, it also has certain drawbacks that cause resentment in some people. Hate speech is another possibility. Hate speech impacts the community when it contains insulting or threatening language. Before it spreads, this kind of speech has to be identified and deleted from social media platforms. The process of determining whether a text's feelings reflect hatred or not involves sentiment analysis. Python language was used to analyze the Twitter dataset. There were 5000 Tweets in total in this dataset, and we used deep learning to improve the machine learning model's accuracy. The experimental outcome in both cases of the Twitter dataset uses the Random Forest approach, which has a 99 percent accuracy rate.

The In-Situ Spectroscopy to Investigate the Electrochemical Reduction of CO2

The in situ attenuated total reflection Fourier transform infrared spectroscopic (FTIR) [1-3], dating back to the 90s’ of last decades, is the most powerful approach to real time character the oxygen-containing group on the catalyst during the electrochemical reduction CO2 reaction. The FTIR offers platform for tackle the greenhouse effect steming form continuous increase of CO2 emissions. Though the monitoring real time the attenuated total reflection Fourier transform infrared spectroscopy of the catalyst and intermediate products separately, we can acquire the information of the intermediate products and variation condition of the reaction system and gain strong evident for further analysis. Therefore, the FTIR possesses the advantages of comprehensive, real-time and high sensitivity [4,5].

Da Animação Performativa à Produção Cinematográfica

A few years ago, motion capture was a technology used exclusively in high-cost film productions. With technological developments, this type of resource has become more accessible to the general public, and so the need arose to look for appropriate methods that serve as an impetus for the development of 3D animations in real time, either through open source software, through equipment that is more accessible to the general public. The importance here lies in reducing costs and discarding any dependency on rendering in 3D animation.This article aims to expose some areas of study associated with performance animation and film production. As a case study, this article presents the development of a prototype for a multimedia installation within the scope of the short-film Animata. With this prototype, it´s intended to explore the limits of the animated character in real time, through the integration between different devices and software, connected in a multimedia ecosystem.

Creating Playable Social Experiences through Whole-Body Interaction with Virtual Characters

This paper describes work towards the goal of enabling unscripted interaction with non-player characters in virtual environments. We hypothesize that we can define a layer of social affordances, based on physical and non-verbal signals exchanged between individuals and groups, which can be reused across games. We have implemented a first version of that substrate that employs whole body interaction with virtual characters and generates nuanced, real-time character performance in response. We describe the playable experience produced by the system, the implementation architecture (based on the behavior specification technology used in Façade, the social model employed in Prom Week, and gesture recognition technology), and illustrate the key behaviors and programming idioms that enable character performance. These idioms include orthogonal coding of attitudes and activities, use of relational rules to nominate social behavior, use of volition rules to rank options, and priority based interleaving of character animations.

СПОСОБИ ВИКОРИСТАННЯ НЕЙРОННИХ МЕРЕЖ ТА МАШИННОГО НАВЧАННЯ В КОМП’ЮТЕРНИХ ІГРАХ

This article discusses the main ways of using neural networks and machine learning methods of various types in computer games. Machine learning and neural networks are hot topics in many technology fields. One of them is the creation of computer games, where new tools are used to make games more interesting. Remastered and modified games with neural networks have become a new trend. One of the most popular ways to implement artificial intelligence is neural networks. They are used in everything from medicine to the entertainment industry. But one of the most promising areas for their development is games. The game world is an ideal platform for testing artificial intelligence without the danger of harming nature or people. Making bots more complex is just a small part of what neural networks can do. They are also actively used in game development, and in some areas they already make people feel uncomfortable. Research is ongoing on color and light correction, real-time character animation and behavior control. The main types of neural networks that can learn such functions are considered. Neural networks learn (and self-learn) very quickly. The more primitive the task, the faster the person will become unnecessary. This is already noticeable in the gaming industry, but will soon spread to other areas of life, because games are just a convenient platform for experimenting with artificial intelligence before its implementation in real life. The main problem faced by scientists is that it is difficult for neural networks to copy the mechanics of the game. There are some achievements in this direction, but research continues. Therefore, in the future, real specialists will be required for the development of games for a long time, although AI is already coping with some tasks.

ILBPSDNet: Based on improved local binary pattern shallow deep convolutional neural network for character recognition

This paper proposes an architecture based on the improved local binary pattern (LBP) shallow deep convolution neural network, which integrates hand-crafted feature pre-processing and the advantage of character learning in the supervised high-level function of CNN, in order to enhance its performance. This study introduced the information of scale space into the LBP to reduce the sensitivity to noise, and applied feature maps with two features, the maximum selection feature map (MLBP) and the first selection feature map (FLBP). The former selected the edge with the strongest intensity to reduce the influence of noise points, while the latter measured local binary features through the scale detection of an effective edge. In the network architecture design, according to the differences of input features, networks of different depths were used for learning, and the features learned by the two networks were adopted for classification. The experimental results show that, the ILBPSDNet proposed had certain recognition abilities in many character data sets, and the network parameters and computation were also reduced. Therefore, it has a significant effect in realizing the application of real-time character recognition. Finally, compared with other latest networks, its network performance could be maintained at a certain level.

Vehicle logo detection based on deep convolutional networks

For intelligent transportation systems, vehicle logo is an important part in the research of vehicle information recognition. Deep convolutional neural networks (CNNs) have been more reasonable and stronger than artificial selection in feature extraction and expression for targets. In our previous work, we found that the network of feature extraction and the training policy of detection have great effects on the accuracy of vehicle logo. For improving small-scale object detecting precision, we change the training policy. According to our previous work, we propose a lightweight network structure with separable convolution to improve the real-time character for vehicle logo while implement the method in embedded devices. The experiment proves that our model can effectively improve the detection accuracy of vehicle logo. Our training policy is valid method for small-scale objects. The lightweight network can solve the equilibrium problem of detecting precision and speed.

Gated neural network framework for interactive character control

Recently, interactive character control models based on neural network have become a hot research topic in computer graphics and motion synthesis. A real-time interactive character control model with two substructures called gated neural network is proposed in this paper. In the first part of the model, a gated network is used to calculate the expert weights based on the user’s input parameters and the character’s current pose dynamically. Mixture of experts is used in choosing different control strategies for user input. The character posture is controlled by selecting mode-adjustment or phase-adjustment. In the second part, a simple neural network is used to adjust the character’s state through additional input parameters on different landscapes and to synthesize the final character motion. Experimental results show that the model can improve the performance of character control.

DReCon

Interactive control of self-balancing, physically simulated humanoids is a long standing problem in the field of real-time character animation. While physical simulation guarantees realistic interactions in the virtual world, simulated characters can appear unnatural if they perform unusual movements in order to maintain balance. Therefore, obtaining a high level of responsiveness to user control, runtime performance, and diversity has often been overlooked in exchange for motion quality. Recent work in the field of deep reinforcement learning has shown that training physically simulated characters to follow motion capture clips can yield high quality tracking results. We propose a two-step approach for building responsive simulated character controllers from unstructured motion capture data. First, meaningful features from the data such as movement direction, heading direction, speed, and locomotion style, are interactively specified and drive a kinematic character controller implemented using motion matching. Second, reinforcement learning is used to train a simulated character controller that is general enough to track the entire distribution of motion that can be generated by the kinematic controller. Our design emphasizes responsiveness to user input, visual quality, and low runtime cost for application in video-games.

Direct delta mush skinning and variants

A significant fraction of the world's population have experienced virtual characters through games and movies, and the possibility of online VR social experiences may greatly extend this audience. At present, the skin deformation for interactive and real-time characters is typically computed using geometric skinning methods. These methods are efficient and simple to implement, but obtaining quality results requires considerable manual "rigging" effort involving trial-and-error weight painting, the addition of virtual helper bones, etc. The recently introduced Delta Mush algorithm largely solves this rig authoring problem, but its iterative computational approach has prevented direct adoption in real-time engines. This paper introduces Direct Delta Mush, a new algorithm that simultaneously improves on the efficiency and control of Delta Mush while generalizing previous algorithms. Specifically, we derive a direct rather than iterative algorithm that has the same ballpark computational form as some previous geometric weight blending algorithms. Straightforward variants of the algorithm are then proposed to further optimize computational and storage cost with insignificant quality losses. These variants are equivalent to special cases of several previous skinning algorithms. Our algorithm simultaneously satisfies the goals of reasonable efficiency, quality, and ease of authoring. Further, its explicit decomposition of rotational and translational effects allows independent control over bending versus twisting deformation, as well as a skin sliding effect.

Modelling and analysis of real-time and reliability for WSN-based CPS

WSN-based CPS can be used to collect information from remote environment and control it. Delay and dependability directly affect the running quality of WSN-based CPS. In this paper, we present a specified process calculus to model the running of the system, including its real-time and reliability character. Then we design a group of algorithms to calculate the reliability of the system under certain responsible time constraint. Using these methods, we can formally depict the running of WSN-based CPS, and can assess real-time and reliability of WSN-based CPS quantitatively. We illustrate our ideas through bank night automatic monitoring and emergency system, and we can improve the system's reliability from 0.58 to 0.87 and higher gradually.

Torso height optimization for bipedal locomotion

Bipedal robots can be better alternatives to other robots in certain applications, but their full potential can only be used if their entire kinematic range is cleverly exploited. Generating motions that are not only dynamically feasible but also take into account the kinematic limits as well as collisions in real time is one of the main challenges towards that goal. We present an approach to generate adaptable torso height trajectories to exploit the full kinematic range in bipedal locomotion. A simplified 2D model approximates the robot’s full kinematic model for multiple steps ahead. It is used to optimize the torso height trajectories while taking future motion kinematics into account. The method significantly improves the robot’s motion not only while walking in uneven terrain, but also during normal walking. Furthermore, we integrated the method in our framework for autonomous walking and we validated its real-time character in successfully conducted experiments.

Kinematic optimization for bipedal robots: a framework for real-time collision avoidance

Bipedal locomotion is more than dynamically stable walking. The redundant kinematic design of humanoid robots allows for complex motions in complex scenarios. One challenge of current robotic research is the exploitation of the capacities of redundant robots in real-time applications. In this paper, we present and evaluate methods for real-time motion generation of redundant robots. The proposed methods are based on a model-predictive approach. We propose and compare methods for optimization of robot motions defined by parameterized task-space trajectories and for redundancy resolution. The approaches are successfully combined in a novel algorithm. The methods are introduced with the help of a minimal model. It shows their applicability for a wide range of complex robotic systems. We apply and validate their effectiveness and their real-time character in several experiments with different environments with the humanoid robot Lola.

Master of Puppets: An Animation-by-Demonstration Computer Puppetry Authoring Framework

This paper presents Master of Puppets (MOP), an animation-by-demonstration framework that allows users to control the motion of virtual characters (puppets) in real time. In the first step, the user is asked to perform the necessary actions that correspond to the character’s motions. The user’s actions are recorded, and a hidden Markov model is used to learn the temporal profile of the actions. During the runtime of the framework, the user controls the motions of the virtual character based on the specified activities. The advantage of the MOP framework is that it recognizes and follows the progress of the user’s actions in real time. Based on the forward algorithm, the method predicts the evolution of the user’s actions, which corresponds to the evolution of the character’s motion. This method treats characters as puppets that can perform only one motion at a time. This means that combinations of motion segments (motion synthesis), as well as the interpolation of individual motion sequences, are not provided as functionalities. By implementing the framework and presenting several computer puppetry scenarios, its efficiency and flexibility in animating virtual characters is demonstrated.

Efficient image compression and decompression algorithms for OCR systems

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the ?Nikola Tesla Museum? in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system.