Supplementing Frequency Domain Interpolation Methods for Character Animation

The animation of human characters entails difficulties exceeding those met simulating objects, machines or plants. A person's gait is a product of nature affected by mood and physical condition. Small deviations from natural movement are perceived with ease by an unforgiving audience. Motion capture technology is frequently employed to record human movement. Subsequent playback on a skeleton underlying the character being animated conveys many of the subtleties of the original motion. Played-back recordings are of limited value, however, when integration in a virtual environment requires movements beyond those in the motion library, creating a need for the synthesis of new motion from pre-recorded sequences. An existing approach involves interpolation between motions in the frequency domain, with a blending space defined by a triangle network whose vertices represent input motions. It is this branch of character animation which is supplemented by the methods presented in this thesis, with work undertaken in three distinct areas. The first is a streamlined approach to previous work. It provides benefits including an efficiency gain in certain contexts, and a very different perspective on triangle network construction in which they become adjustable and intuitive user-interface devices with an increased flexibility allowing a greater range of motions to be blended than was possible with previous networks. Interpolation-based synthesis can never exhibit the same motion variety as can animation methods based on the playback of rearranged frame sequences. Limitations such as this were addressed by the second phase of work, with the creation of hybrid networks. These novel structures use properties of frequency domain triangle blending networks to seamlessly integrate playback-based animation within them. The third area focussed on was distortion found in both frequency- and time-domain blending. A new technique, single-source harmonic switching, was devised which greatly reduces it, and adds to the benefits of blending in the frequency domain

Reducing animator keyframes

The aim of this doctoral thesis is to present a body of work aimed at reducing the time spent by animators manually constructing keyframed animation. To this end we present a number of state of the art machine learning techniques applied to the domain of character animation. Data-driven tools for the synthesis and production of character animation have a good track record of success. In particular, they have been adopted thoroughly in the games industry as they allow designers as well as animators to simply specify the high-level descriptions of the animations to be created, and the rest is produced automatically. Even so, these techniques have not been thoroughly adopted in the film industry in the production of keyframe based animation [Planet, 2012]. Due to this, the cost of producing high quality keyframed animation remains very high, and the time of professional animators is increasingly precious. We present our work in four main chapters. We first tackle the key problem in the adoption of data-driven tools for key framed animation - a problem called the inversion of the rig function. Secondly, we show the construction of a new tool for data-driven character animation called the motion manifold - a representation of motion constructed using deep learning that has a number of properties useful for animation research. Thirdly, we show how the motion manifold can be extended as a general tool for performing data-driven animation synthesis and editing. Finally, we show how these techniques developed for keyframed animation can also be adapted to advance the state of the art in the games industry

Exploring Sparse, Unstructured Video Collections of Places

The abundance of mobile devices and digital cameras with video capture makes it easy to obtain large collections of video clips that contain the same location, environment, or event. However, such an unstructured collection is difficult to comprehend and explore. We propose a system that analyses collections of unstructured but related video data to create a Videoscape: a data structure that enables interactive exploration of video collections by visually navigating — spatially and/or temporally — between different clips. We automatically identify transition opportunities, or portals. From these portals, we construct the Videoscape, a graph whose edges are video clips and whose nodes are portals between clips. Now structured, the videos can be interactively explored by walking the graph or by geographic map. Given this system, we gauge preference for different video transition styles in a user study, and generate heuristics that automatically choose an appropriate transition style. We evaluate our system using three further user studies, which allows us to conclude that Videoscapes provides significant benefits over related methods. Our system leads to previously unseen ways of interactive spatio-temporal exploration of casually captured videos, and we demonstrate this on several video collections

Exploiting Novel Deep Learning Architecture in Character Animation Pipelines

This doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production

Exploiting Novel Deep Learning Architecture in Character Animation Pipelines

This doctoral dissertation aims to show a body of work proposed for improving different blocks in the character animation pipelines resulting in less manual work and more realistic character animation. To that purpose, we describe a variety of cutting-edge deep learning approaches that have been applied to the field of human motion modelling and character animation. The recent advances in motion capture systems and processing hardware have shifted from physics-based approaches to data-driven approaches that are heavily used in the current game production frameworks. However, despite these significant successes, there are still shortcomings to address. For example, the existing production pipelines contain processing steps such as marker labelling in the motion capture pipeline or annotating motion primitives, which should be done manually. In addition, most of the current approaches for character animation used in game production are limited by the amount of stored animation data resulting in many duplicates and repeated patterns. We present our work in four main chapters. We first present a large dataset of human motion called MoVi. Secondly, we show how machine learning approaches can be used to automate proprocessing data blocks of optical motion capture pipelines. Thirdly, we show how generative models can be used to generate batches of synthetic motion sequences given only weak control signals. Finally, we show how novel generative models can be applied to real-time character control in the game production

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility