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Recently, the world has been gaining vastly increasing access to more and more advanced artificial intelligence tools. This phenomenon does not bypass the world of sound and visual art, and both of these worlds can benefit in ways yet unexplored, drawing them closer to one another. Recent breakthroughs open possibilities to utilize AI driven tools for creating generative art and using it as a compound of other multimedia. The aim of this paper is to present an original concept of using AI to create a visual compound material to existing audio source. This is a way of broadening accessibility thus appealing to different human senses using source media, expanding its initial form. This research utilizes a novel method of enhancing fundamental material consisting of text audio or text source (script) and sound layer (audio play) by adding an extra layer of multimedia experience – a visual one, generated procedurally. A set of images generated by AI tools, creating a story-telling animation as a new way to immerse into the experience of sound perception and focus on the initial audial material. The main idea of the paper consists of creating a pipeline, form of a blueprint for the process of procedural image generation based on the source context (audial or textual) transformed into text prompts and providing tools to automate it by programming a set of code instructions. This process allows creation of coherent and cohesive (to a certain extent) visual cues accompanying audial experience levering it to multimodal piece of art. Using nowadays technologies, creators can enhance audial forms procedurally, providing them with visual context. The paper refers to current possibilities, use cases, limitations and biases giving presented tools and solutions.

Minor bodies exhibit considerable variability in shape and surface morphology, posing challenges for spacecraft operations, which are further compounded by highly non-linear dynamics and limited communication windows with Earth. Additionally, uncertainties persist in the shape and surface morphology of minor bodies due to errors in ground-based estimation techniques. The growing need for autonomy underscores the importance of robust image processing and visual-based navigation methods. To address this demand, it is essential to conduct tests on a variety of body shapes and with different surface morphological features. This work introduces the procedural Minor bOdy geNErator Tool (MONET), implemented using an open-source 3D computer graphics software. The starting point of MONET is the three-dimensional mesh of a generic minor body, which is procedurally modified by introducing craters, boulders, and surface roughness, resulting in a photorealistic model. MONET offers the flexibility to generate a diverse range of shapes and surface morphological features, aiding in the recreation of various minor bodies. Users can fine-tune relevant parameters to create the desired conditions based on the specific application requirements. The tool offers the capability to generate two default families of models: rubble-pile, characterized by numerous different-sized boulders, and comet-like, reflecting the typical morphology of comets. MONET serves as a valuable resource for researchers and engineers involved in minor body exploration missions and related projects, providing insights into the adaptability and effectiveness of guidance and navigation techniques across a wide range of morphological scenarios. [ABSTRACT FROM AUTHOR]

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field. [ABSTRACT FROM AUTHOR]

The University of Johannesburg has integrated serious games into its teaching, exemplified by Codebreakers, a 2D game teaching information theory. While successful, Codebreakers lacked personalisation and used a criticised assessment method based on answer streaks. Knowledge tracing algorithms, known for their effectiveness in intelligent tutoring systems, were considered to address these limitations. This led to the research question: "Can a new serious game be designed, incorporating knowledge tracing algorithms to deliver personalised learning experiences in telecommunications education?" In response, an escape-themed serious game was developed, integrating Bayesian Knowledge Tracing as a statistical student model for personalised learning. This innovative approach combines free-roam gameplay with tailored educational content, significantly advancing serious game design. While primarily aimed at enhancing Codebreakers, this new game contributes substantially to serious game theory by successfully implementing personalised learning within an engaging format. The project showcases the potential of knowledge tracing algorithms in creating adaptive, student-centered learning experiences within the context of educational games. [ABSTRACT FROM AUTHOR]

Recognition of human actions using machine learning requires extensive datasets to develop robust models. Nevertheless, obtaining real-world data presents challenges due to the costly and time-consuming process involved. Additionally, existing datasets mostly contain indoor videos due to the challenges of capturing pose data outdoors. Synthetic data have been used to overcome these difficulties, yet the currently available synthetic datasets for human action recognition lack photorealism and diversity in their features. Addressing these shortcomings, we develop the NOVAction engine to generate highly diversified and photorealistic synthetic human action sequences. We use NOVAction to create the NOVAction23 dataset comprising 25,415 human action sequences with corresponding poses and labels (available at https://github.com/celikcan-cglab/NOVAction23). In NOVAction23, the performed motions and viewpoints are varied on the fly through procedural generation, to ensure that, for a given action class, each generated sequence features a distinct motion performed by one of the 1,105 synthetic humans captured from a unique viewpoint. Moreover, each synthetic human is unique in terms of body shape (height and weight), skin tone, gender, hair, facial hair, clothing, shoes and accessories. To further increase data diversity, the motion sequences are rendered under various weather conditions and at different times of day, across three outdoor and two indoor settings. We evaluate NOVAction23 by training three state-of-the-art recognizers on it, in addition to the NTU 120 dataset, and corroborating using real-world videos from YouTube. Our results confirm that the NOVAction23 dataset can improve the performance of state-of-the-art human action recognition. [Display omitted] • NOVAction engine automatically generates massively diverse human action data. • NOVAction23, created by NOVAction, novel dataset of human action data. • Features 25,415 unique synthetic human action sequences with poses and labels. • Performed by 1,105 synthetic humans, each captured from a unique viewpoint. • Improved human action recognition performance using state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

As virtual worlds continue to rise in popularity, so do the expectations of users for the content of virtual scenes. Virtual worlds must be large in scope and offer enough freedom of movement to keep the audience occupied at all times. For content creators, it is difficult to keep up by manually producing the surrounding content. Therefore, the application of procedural modelling techniques is required. Virtual worlds often mimic the real world, which is composed of organized and connected outdoor and indoor layouts. It is expected that all content is present on the virtual scene and that a user can navigate streets, enter buildings, and interact with furniture within a single virtual world. While there are many procedural methods for generating different layout types, they mostly focus only on one layout type, whereas complete scene generation is greatly underrepresented. This paper aims to identify the coverage of layout types by different methods because similar issues exist for the generation of content of different layout types. When creating a new method for layout generation, it is important to know if the results of existing methods can be appended to other methods. This paper presents a survey of existing procedural modelling methods, which were organized into five categories based on the core approach: pure subdivision, grammar‐based, data‐driven, optimization, and simulation. Information about the covered layout types, the possibility of user interaction during the generation process, and the input and output shape types of the generated content is provided for each surveyed method. The input and output shape types of the generated content can be useful to identify which methods can continue the generation by using the output of other methods as their input. It was concluded that all surveyed methods work for only a few different layout types simultaneously. Moreover, only 35% of the surveyed methods offer interaction with the user after completing the initial process of space generation. Most existing approaches do not perform transformations of shape types. A significant number of methods use the irregular shape type as input and generate the same shape type as the output, which is sufficient for coverage of all layout types when generating a complete virtual world. [ABSTRACT FROM AUTHOR]

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation. [ABSTRACT FROM AUTHOR]

Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image. [ABSTRACT FROM AUTHOR]

The University of Johannesburg has integrated serious games into its teaching, exemplified by Codebreakers, a 2D game teaching information theory. While successful, Codebreakers lacked personalisation and used a criticised assessment method based on answer streaks. Knowledge tracing algorithms, known for their effectiveness in intelligent tutoring systems, were considered to address these limitations. This led to the research question: "Can a new serious game be designed, incorporating knowledge tracing algorithms to deliver personalised learning experiences in telecommunications education?" In response, an escape-themed serious game was developed, integrating Bayesian Knowledge Tracing as a statistical student model for personalised learning. This innovative approach combines free-roam gameplay with tailored educational content, significantly advancing serious game design. While primarily aimed at enhancing Codebreakers, this new game contributes substantially to serious game theory by successfully implementing personalised learning within an engaging format. The project showcases the potential of knowledge tracing algorithms in creating adaptive, student-centered learning experiences within the context of educational games.

We present a method for interactively authoring and simulating meandering river networks. Starting from a terrain with an initial low-resolution network encoded as a directed graph, we simulate the evolution of the path of the different river channels using a physically-based migration equation augmented with control terms. The curvature-based terms in the equation allow us to reproduce phenomena identified in geomorphology, such as downstream migration of bends. Control terms account for the influence of the landscape topography and user-defined river trajectory constraints. Our model implements abrupt events that shape meandering networks, such as cutoffs forming oxbow lakes and avulsions. We visually show the effectiveness of our method and compare the generated networks quantitatively to river data by analyzing sinuosity and wavelength metrics. Our vector-based model runs at interactive rates, allowing for efficient authoring of large-scale meandering networks. [ABSTRACT FROM AUTHOR]

Autonomous robots can be employed in exploring unknown environments and performing many tasks, such as, e.g. detecting areas of interest, collecting target objects, etc. Deep reinforcement learning (RL) is often used to train this kind of robot. However, concerning the artificial environments aimed at testing the robot, there is a lack of available data sets and a long time is needed to create them from scratch. A good data set is in fact usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper, we focus on the specification of the properties of the solutions needed to build a data set, making the case of environment exploration. In the proposed approach, rather than using imperative programming, we explore the possibility of generating data sets using constraint programming in Prolog. In this phase, geometric predicates describe a virtual environment according to inter-space requirements. The second part of the paper is focused on testing the generated data set in an AI gym via space search techniques. We developed a Neuro-Symbolic agent built from the following: (i) A deep Q-learning component implemented in Python, able to address via RL a search problem in the virtual space; the agent has the goal to explore a generated virtual environment to seek for a target, improving its performance through a RL process. (ii) A symbolic component able to re-address the search when the Q-learning component gets stuck in a part of the virtual environment; these components stimulate the agent to move to and explore other parts of the environment. Wide experimentation has been performed, with promising results, and is reported, to demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Minor bodies exhibit considerable variability in shape and surface morphology, posing challenges for spacecraft operations, which are further compounded by highly non-linear dynamics and limited communication windows with Earth. Additionally, uncertainties persist in the shape and surface morphology of minor bodies due to errors in ground-based estimation techniques. The growing need for autonomy underscores the importance of robust image processing and visual-based navigation methods. To address this demand, it is essential to conduct tests on a variety of body shapes and with different surface morphological features. This work introduces the procedural Minor bOdy geNErator Tool (MONET), implemented using an open-source 3D computer graphics software. The starting point of MONET is the three-dimensional mesh of a generic minor body, which is procedurally modified by introducing craters, boulders, and surface roughness, resulting in a photorealistic model. MONET offers the flexibility to generate a diverse range of shapes and surface morphological features, aiding in the recreation of various minor bodies. Users can fine-tune relevant parameters to create the desired conditions based on the specific application requirements. The tool offers the capability to generate two default families of models: rubble-pile, characterized by numerous different-sized boulders, and comet-like, reflecting the typical morphology of comets. MONET serves as a valuable resource for researchers and engineers involved in minor body exploration missions and related projects, providing insights into the adaptability and effectiveness of guidance and navigation techniques across a wide range of morphological scenarios.

A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field.

There is a growing dwelling problem on a global scale. The crisis is getting more severe each day with incidents like war and pandemics, which resulted in some groups seeking alternative life scenarios. Therefore, expanding the research related to housing and answering these needs become obligatory. Game engines and procedural generation have been used to rapidly represent solutions with their high-quality renderings for these kinds of situations. This paper aims to present a user-friendly container settlement generation tool using procedural generation, developed using the Unity 3D game engine, focusing on various real-life scenarios based on contextual and financial parameters. Along with the tool’s development and functionalities, the paper presents a case study with ecological and post-disaster scenario presets in the Marmara and Mediterranean Regions of Türkiye to demonstrate its applicability in the settlement generation process.

The maintenance of visual appeal and coherence in the procedural game scene generation is still a difficult problem. Traditional procedural game scene generation algorithms produce samples that show a noticeable resemblance to each other. The proposed algorithm allows us to add diverse game object compositions and increase creativity value in that way. Result diversity is formed by the proposed genetic algorithm modification and MCDM method based on the fitness function. Video game immersion is reached by aesthetic game element pattern composition, and one of the solutions for this issue is to apply automated aesthetic modelling of the generated game levels. In this research, the construction of fitness function was extended by the modelling of aesthetic principles, which were reverse-engineered from Gestalt principles. All rules were implemented by construction of a focal function with a square zone for each matrix cell of the single game scene. Five types of Gestalt rules were modelled and combined into a Pythagorean neutrosophic WASPAS method and the final score calculation algorithm was proposed. The proposed approach to generating game scenes strikes a balance between functionality and aesthetics to provide players with an engaging and immersive gaming experience. [ABSTRACT FROM AUTHOR]

Educators and students have shown significant interest in the potential for generative artificial intelligence (AI) technologies to support student learning outcomes, for example, by offering personalized experiences, 24 h conversational assistance, text editing and help with problem-solving. We review contemporary perspectives on the value of AI as a tool in an educational context and describe our recent research with undergraduate students, discussing why and how we integrated OpenAI tools ChatGPT and Dall-E into the curriculum during the 2022–2023 academic year. A small cohort of games programming students in the School of Computing and Digital Media at London Metropolitan University was given a research and development assignment that explicitly required them to engage with OpenAI. They were tasked with evaluating OpenAI tools in the context of game development, demonstrating a working solution and reporting on their findings. We present five case studies that showcase some of the outputs from the students and we discuss their work. This mode of assessment was both productive and popular, mapping to students' interests and helping to refine their skills in programming, problem-solving, critical reflection and exploratory design. [ABSTRACT FROM AUTHOR]

Three-dimensional models, reconstructed from real-life objects, are extensively used in virtual and mixed reality technologies. In this paper we propose an approach to 3D model reconstruction via inverse procedural modeling and describe two variants of this approach. The first option is to fit a set of input parameters using a genetic algorithm. The second option allows us to significantly improve precision by using gradients within the memetic algorithm, differentiable rendering, and differentiable procedural generators. We demonstrate the results of our work on different models, including trees, which are complex objects that most existing methods cannot reconstruct. In our work, we see two main contributions. First, we propose a method to join differentiable rendering and inverse procedural modeling. This gives us the ability to reconstruct 3D models more accurately than existing approaches when few input images are available, even for a single image. Second, we combine both differentiable and non-differentiable procedural generators into a single framework that allows us to apply inverse procedural modeling to fairly complex generators. We show that both variants of our approach can be useful: the differentiable one is more precise but puts limitations on the procedural generator, while the one based on genetic algorithms can be used with any existing generator. The proposed approach uses information about the symmetry and structure of the object to achieve high-quality reconstruction from a single image.

Copyright of ESTOA: Revista de la Facultad de Arquitectura y Urbanismo de la Universidad de Cuenca is the property of ESTOA Revista de la Facultad de Arquitectura y Urbanismo de la Universidad de Cuenca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation.

Educators and students have shown significant interest in the potential for generative artificial intelligence (AI) technologies to support student learning outcomes, for example, by offering personalized experiences, 24 h conversational assistance, text editing and help with problem-solving. We review contemporary perspectives on the value of AI as a tool in an educational context and describe our recent research with undergraduate students, discussing why and how we integrated OpenAI tools ChatGPT and Dall-E into the curriculum during the 2022–2023 academic year. A small cohort of games programming students in the School of Computing and Digital Media at London Metropolitan University was given a research and development assignment that explicitly required them to engage with OpenAI. They were tasked with evaluating OpenAI tools in the context of game development, demonstrating a working solution and reporting on their findings. We present five case studies that showcase some of the outputs from the students and we discuss their work. This mode of assessment was both productive and popular, mapping to students’ interests and helping to refine their skills in programming, problem-solving, critical reflection and exploratory design.

We present a vision-based algorithm that uses spatio-temporal satellite imagery, pattern recognition, procedural modeling, and deep learning to perform tree localization in urban settings. Our method resolves two primary challenges. First, automated city-scale tree localization at high accuracy typically requires significant acquisition/user intervention. Second, vegetation-index segmentation methods from satellites require manual thresholding, which varies across geographic areas, and are not robust across cities with varying terrain, geometry, altitude, and canopy. In our work, we compensate for the lack of visual detail by using satellite snapshots across twelve months and segment cities into various vegetation clusters. Then, we use multiple GAN-based networks to plant trees by recognizing placement patterns inside segmented regions procedurally. We present comprehensive experiments over four cities (Chicago, Austin, Indianapolis, Lagos), achieving tree count accuracies of 87–97%. Finally, we show that the knowledge accumulated from each model (trained on a particular city) can be transferred to a different city. [ABSTRACT FROM AUTHOR]

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept. [ABSTRACT FROM AUTHOR]

Copyright of Mugla Journal of Science & Technology is the property of Mugla Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article analyses discourses around procedural content generation (PCG) as automation of creativity in the games industry. PCG refers to techniques for creating game content algorithmically, by manipulating data through sets of computational operations and parameters. By producing scalable results with combinatorial diversity, procedural generation is touted as the future of content, yet flouted as the harbinger of technological unemployment in game art production. Critical scholarship on automation suggests that the real danger is not job loss per se, but the constitution of an underclass of artists whose vital work of conditioning algorithmic outputs is denigrated as derivative and 'manual'. Framed by liberal humanist ideas of agency, PCG naturalizes trade-offs where the autonomy of generative machines is contingent upon the automatism of its human conditioners. This qualitative analyses of talks on PCG at the Game Developers Conference (2015–2020) shows how procedural systems bifurcate the creative work of algorithmic cultural production into affective and mechanical forms of conditioning that map onto stratifications of racial capitalism. Affective tuning resists documentation and is reserved for artists with technomasculine forms of cultural capital; mechanical tuning is relegated to automatable and outsourced labour and relies on replicable technique that is considered artistic but not creative. This article argues that PCG's reclassification of creativity through racialised dialectics of human agency and machine automaticity overlooks the autonomy of procedural systems. PCG pipelines are organised less around the agency of human toolmakers and more around the autonomy of systems that assimilate tasks in the management of complex networks of dependencies. Instead of pitting artists against machines, this analysis politicises automation's racial stratifications by examining the momentum of more-than-human systems in which toolmakers and tool users negotiate granularities of control and degrees of concession. [ABSTRACT FROM AUTHOR]

The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework's effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users. [ABSTRACT FROM AUTHOR]

In this paper, we try to solve the problem of making video games with multiple storylines. To do this, we developed an algorithm capable of generating variations in the game's story through altering the behaviors of the characters. For this task, we use Q-Learning. Our results indicate that this algorithm may be viable to be used in video games for commercial purposes.

We present TreePartNet, a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural neural decomposition exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods. [ABSTRACT FROM AUTHOR]