3DCGキャラクタの表現の改善法と実時間操作に関する研究

早大学位記番号:新8176早稲田大

Hierarchical multiple output gaussian processes for human motion data

Diferentes aplicaciones estadísticas implican el uso de diferentes parámetros y observaciones que en muchos casos están relacionadas de alguna manera dependiendo de la estructura del problema. Usualmente, estos parametros son usados como variables que codifican cierta información relacionada con las observaciones, además, ya estos parametros no son observables ni tampoco pueden ser medidos directamente, son inferidos de los datos observados gracias a las correlaciones dadas entre los mismos. De esa manera, se vuelve natural el modelar el fenómeno por medio de una estructura jerárquica en donde las variables observadas esten condicionadas a los parámetros, y a su vez estos parámetros condicionados a hiperparámetros, etc. Este tipo de modelos son relevantes en el sentido de que sirven cómo buenas aproximaciones al comportamiento de los datos. En el caso de regresión, modelos no paramétricos cómo los procesos Gausianos han sido propuestos también con algún tipo de estructura jerárquica, la cuál depende del problema a ser estudiado. Diferentes modelos jerárquicos han sido propuestos. Recientemente un novedoso método jerárquico para procesos Gausianos fue propuesto, en dicho modelo, se asumen que existen diferentes señales observadas que están relacionadas por una tendencia común a todas estas observaciones, la cuál puede ser predecida. Así, las señales observadas pueden ser vistas como versiones corruptas de esa tendencia común. Sin embargo, este tipo de modelos solo ha sido desarrollado para modelos de una sola salida, de esa manera se vuelve interesante explorar una extensión de este modelo a multiples salidas. Por tal motivo, en este trabajo se presenta una extension de un Proceso Gausiano jerÃarquico a multiples salidas, usando funciones de covarianza existentes con el objetivo de hacer interpolación y síntesis de movimiento humano. El modelo fue probado con datos tanto artificiales cómo reales, los resultados muestran que el modelo es exitoso interpolando y sintetizando movimiento humano en comparación a un modelo de procesos Gausianos de multiples salidas simple el cuál se usa en este trabajo como referencia

The Machine as Art/ The Machine as Artist

The articles collected in this volume from the two companion Arts Special Issues, “The Machine as Art (in the 20th Century)” and “The Machine as Artist (in the 21st Century)”, represent a unique scholarly resource: analyses by artists, scientists, and engineers, as well as art historians, covering not only the current (and astounding) rapprochement between art and technology but also the vital post-World War II period that has led up to it; this collection is also distinguished by several of the contributors being prominent individuals within their own fields, or as artists who have actually participated in the still unfolding events with which it is concerne

Gesture-Based Robot Path Shaping

For many individuals, aging is frequently associated with diminished mobility and dexterity. Such decreases may be accompanied by a loss of independence, increased burden to caregivers, or institutionalization. It is foreseen that the ability to retain independence and quality of life as one ages will increasingly depend on environmental sensing and robotics which facilitate aging in place. The development of ubiquitous sensing strategies in the home underpins the promise of adaptive services, assistive robotics, and architectural design which would support a person\u27s ability to live independently as they age. Instrumentation (sensors and processing) which is capable of recognizing the actions and behavioral patterns of an individual is key to the effective component design in these areas. Recognition of user activity and the inference of user intention may be used to inform the action plans of support systems and service robotics within the environment. Automated activity recognition involves detection of events in a sensor data stream, conversion to a compact format, and classification as one of a known set of actions. Once classified, an action may be used to elicit a specific response from those systems designed to provide support to the user. It is this response that is the ultimate use of recognized activity. Hence, the activity may be considered as a command to the system. Extending this concept, a set of distinct activities in the form of hand and arm gestures may form the basis of a command interface for human-robot interaction. A gesture-based interface of this type promises an intuitive method for accessing computing and other assistive resources so as to promote rapid adoption by elderly, impaired, or otherwise unskilled users. This thesis includes a thorough survey of relevant work in the area of machine learning for activity and gesture recognition. Previous approaches are compared for their relative benefits and limitations. A novel approach is presented which utilizes user-generated feedback to rate the desirability of a robotic response to gesture. Poorly rated responses are altered so as to elicit improved ratings on subsequent observations. In this way, responses are honed toward increasing effectiveness. A clustering method based on the Growing Neural Gas (GNG) algorithm is used to create a topological map of reference nodes representing input gesture types. It is shown that learning of desired responses to gesture may be accelerated by exploiting well-rewarded actions associated with reference nodes in a local neighborhood of the growing neural gas topology. Significant variation in the user\u27s performance of gestures is interpreted as a new gesture for which the system must learn a desired response. A method for allowing the system to learn new gestures while retaining past training is also proposed and shown to be effective

Fabricate 2020

Fabricate 2020 is the fourth title in the FABRICATE series on the theme of digital fabrication and published in conjunction with a triennial conference (London, April 2020). The book features cutting-edge built projects and work-in-progress from both academia and practice. It brings together pioneers in design and making from across the fields of architecture, construction, engineering, manufacturing, materials technology and computation. Fabricate 2020 includes 32 illustrated articles punctuated by four conversations between world-leading experts from design to engineering, discussing themes such as drawing-to-production, behavioural composites, robotic assembly, and digital craft

Undergraduate and Graduate Course Descriptions, 2021 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2021

Undergraduate and Graduate Course Descriptions, 2021 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2021

Undergraduate and Graduate Course Descriptions, 2023 Spring

Wright State University undergraduate and graduate course descriptions from Spring 2023