Pathway to Future Symbiotic Creativity

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed

A Motion Control Scheme for Animating Expressive Arm Movements

Current methods for figure animation involve a tradeoff between the level of realism captured in the movements and the ease of generating the animations. We introduce a motion control paradigm that circumvents this tradeoff-it provides the ability to generate a wide range of natural-looking movements with minimal user labor. Effort, which is one part of Rudolf Laban\u27s system for observing and analyzing movement, describes the qualitative aspects of movement. Our motion control paradigm simplifies the generation of expressive movements by proceduralizing these qualitative aspects to hide the non-intuitive, quantitative aspects of movement. We build a model of Effort using a set of kinematic movement parameters that defines how a figure moves between goal keypoints. Our motion control scheme provides control through Effort\u27s four dimensional system of textual descriptors, providing a level of control thus far missing from behavioral animation systems and offering novel specification and editing capabilities on top of traditional keyframing and inverse kinematics methods. Since our Effort model is inexpensive computationally, Effort-based motion control systems can work in real-time. We demonstrate our motion control scheme by implementing EMOTE (Expressive MOTion Engine), a character animation module for expressive arm movements. EMOTE works with inverse kinematics to control the qualitative aspects of end-effector specified movements. The user specifies general movements by entering a sequence of goal positions for each hand. The user then expresses the essence of the movement by adjusting sliders for the Effort motion factors: Space, Weight, Time, and Flow. EMOTE produces a wide range of expressive movements, provides an easy-to-use interface (that is more intuitive than joint angle interpolation curves or physical parameters), features interactive editing, and real-time motion generation

Thai dance knowledge archive framework based on Labanotation represented in 3D animation

© 2017 IEEE.Southeast Asia is one of the most rapidly growing regions in the world with natural and cultural resources. It is important to pass on the cultural knowledge to the next generation. Intangible Cultural Heritage like traditional dances, or folk dance is a valuable cultural knowledge to be maintained and passed on by transferring tacit knowledge, and even explicit knowledge such as books, or video presentations. Issues of passing on the knowledge can be the loss of knowledge from time to time by the reduction of the number of dance masters, unreliable sources, and low quality. To retrieve such valuable knowledge, there is a widely-used tool in Europe, in the United States, Asia and Southeast Asia, called 'Labanotation' which is about recording and analyzing the dance movement. This paper focuses on proposing a framework for a traditional Thai dance knowledge archive creating an ontology using knowledge engineering based on Labanotation by transferring notation scores to represent the dance in 3D Animation. The framework assists dancers, notators, knowledge engineers, software engineers to successfully communicate with each other

Using music and motion analysis to construct 3D animations and visualisations

This paper presents a study into music analysis, motion analysis and the integration of music and motion to form creative natural human motion in a virtual environment. Motion capture data is extracted to generate a motion library, this places the digital motion model at a fixed posture. The first step in this process is to configure the motion path curve for the database and calculate the possibility that two motions were sequential through the use of a computational algorithm. Every motion is then analysed for the next possible smooth movement to connect to, and at the same time, an interpolation method is used to create the transitions between motions to enable the digital motion models to move fluently. Lastly, a searching algorithm sifts for possible successive motions from the motion path curve according to the music tempo. It was concluded that the higher ratio of rescaling a transition, the lower the degree of natural motio

Using music and motion analysis to construct 3D animations and visualizations

This paper presents a study into music analysis, motion analysis and the integration of music and motion to form creative natural human motion in a virtual environment. Motion capture data is extracted to generate a motion library, this places the digital motion model at a fixed posture. The first step in this process is to configure the motion path curve for the database and calculate the possibility that two motions were sequential through the use of a computational algorithm. Every motion is then analysed for the next possible smooth movement to connect to, and at the same time, an interpolation method is used to create the transitions between motions to enable the digital motion models to move fluently. Lastly, a searching algorithm sifts for possible successive motions from the motion path curve according to the music tempo. It was concluded that the higher ratio of rescaling a transition, the lower the degree of natural motion

Fuzzy transfer learning in human activity recognition.

Assisted living environments are incorporated with different technological solutions to improve the quality of life and well-being. In recent years, there has been a growing interest in the research community on how to develop evolving solutions to aid assisted living. Different techniques have been studied to address the need for technological systems which are intelligent enough to evolve their knowledge to solve tasks which have not been previously encountered. One such approach is Transfer Learning (TL), for example, between humans and robots. Humans excel at dealing with everyday activities, learning and adapting to different activities. This comprises different complex techniques which enable the lifelong learning process from observation of our environment. To obtain similar learning in assistive agents, TL is needed. The aim of the research reported in this thesis is to address the challenge associated with learning and reuse of knowledge by assistive agents in an Ambient Assisted Living (AAL) environment. In this thesis, a novel approach to transfer learning of human activities through the combination of three methods; TL, Fuzzy Systems (FS) and Human Activity Recognition (HAR) is presented. Through the incorporation of FS into the proposed approach, uncertainty that is evident in the dynamic nature of human activities are embedded into the learning model. This research is focused on applications in assistive robotics. This is with a purpose of enabling assistive robots in AAL environments to acquire knowledge of such activities as are performed by humans. To achieve this, an extensive investigation into existing learning methods applied in human activities is conducted. The investigation encompasses current state-of-the-art of TL approaches employed in skill transfer across different but contextually related activities. To address the research questions identified in the thesis, the contributions of the methodology employed are in three main categories; 1) Firstly, a novel framework for human activity learning from information observed. Experiments are conducted on selected human activities to acquire enough information for building the framework. From the acquired information, relevant features extracted are used in a learning model to recognise different activities. 2) Secondly, the sequence of occurrence(s) of tasks in an activity needs to be considered in the learning process. Therefore, in this research, a novel technique for adaptive learning of activity sequences from acquired information is developed. 3) Finally, from the sequence obtained, a novel technique for transfer of human activity across heterogeneous feature space existing between a human and an assistive robot is developed. These categories form the basis of the TL framework modelled in this research. The framework proposed is applied to TL of human activity from data generated experimentally and benchmark datasets of various classes of human activities. The results presented in this thesis show that exploring the process of human activity learning is an important aspect in the TL framework. The features extracted sufficiently distinguish relevant patterns for each activity. Also, the results demonstrate the ability of the methodology to learn and predict human actions with a high degree of certainty. This encourages the use of TL in assisted living environments and other applications. This and many more applications of TL in technology would be a potential driver of the next revolution in artificial intelligence