The realistic animation of virtual characters can enhance user experience. Motion-editing methods such as keyframing and motion capture are effective for pre-determined animations but are incapable of real-time generation. Algorithm-based dynamic simulation and machine learning-based motion synthesis are procedural but too complex. This thesis explores an approach known as animation interpolation, which benefits from the strengths of both types of methods. Animation interpolation generates full animation sequences by assembling pre-defined motion primitives or key poses in real-time. <div><br></div><div>The purpose of this thesis is to evaluate the naturalness of character animation in three common interpolation methods: linear Euler interpolation, spherical linear quaternion interpolation, and spherical spline quaternion interpolation. Many researchers have studied the mathematical equations, motion curves, and velocity graphs of these algorithms. This thesis focuses on the perceptual evaluation and the implementation of expressive upper body character animation. </div><div><br></div><div>During the experimental studies, 97 participants watched 12 animation clips of a character performing four different upper body motions using three interpolation methods. The motions were based on McNeill’s classification of body gestures (beat gesture, deictic gesture, iconic gesture, and metaphoric gesture). After viewing each clip, the participants rated the naturalness on a 5-point Likert scale. The results showed that animations generated using spherical spline quaternion interpolation were perceived as significantly more natural than those generated from the other two interpolation methods.</div>

Crowd Simulation and Visualization are an important aspect of many applications such as Movies, Games and Virtual Reality simulations. The advantage with crowd rendering in movies is that the entire rendering process can be done off-line. And hence computational power is not much of an overhead. However, applications like Games and Virtual Reality Simulations demand real-time interactivity. The sheer processing power demanded by real time interactivity has, thus far, limited crowd simulations to specialized equipment. In this thesis we try to address the issue of rendering and visualizing a large crowd of animated figures at interactive rates. Recent trends in hardware capabilities and the availability of cheap, commodity graphics cards capable of general purpose computations have achieved immense computational speed up and have paved the way for this solution. We propose a Graphics Processing Unit(GPU) based implementation for animating virtual characters. However, simulation of a large number of human like characters is further complicated by the fact that it needs to be visually convincing to the user. We suggest a motion graph based animation-splicing approach to achieving this sense of realism. / Master of Science

Understanding and synthesizing locomotion of humans and animals will have far-reaching impacts in computer animation, robotic and biomechanics. However, due to the complexity of the neuromuscular control and physical interactions with the environment, computationally modeling these seemingly effortless locomotion imposes a grand challenge for scientists, engineers and artists. The focus of this thesis is to present a set of computational tools, which can simulate the physical environment and optimize the control strategy, to automatically synthesize locomotion for humans and animals. We first present computational tools to study swimming motions for a wide variety of aquatic animals. This method first builds a simulation of two-way interaction between fluid and an articulated rigid body system. It then searches for the most energy efficient way to swim for a given body shape in the simulated hydrodynamic environment. Next, we present an algorithm that can synthesize locomotion of soft body animals that do not have skeleton support. We combine a finite element simulation with a muscle model that is inspired by muscular hydrostat in nature. We then formulate a quadratic program with complementarity condition (QPCC) to optimize the muscle contraction and contact forces that can lead to meaningful locomotion. We develop an efficient QPCC solver that solves a challenging optimization problem at the presence of discontinuous contact events. We also present algorithms to model human locomotion with a passive mechanical device: riding a bicycle in this case. We apply a powerful reinforcement learning algorithm, which can search for both the parametrization and the parameters of a control policy, to enable a virtual human character to perform bicycle stunts in a physically simulated environment. Finally, we explore the possibility to use the computational tools that are developed for computer animation to control a real robot. We develop a simulation calibration technique which reduces the discrepancy between the simulated results and the performance of the robot in the real environments. For certain motion planning tasks, this method can transfer the controllers optimized for a virtual character in a simulation to a robot that operates in a real environment.

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.

There is a difference between making something move and bringing something to life in animation. The animation principles work together to bring out character that is only visible with memorable movement. Given how the entertainment space has changed and grown over recent years, it is arguably vital for characters to be unforgettable for franchises to survive. This creative thesis explores how personalities are interpreted in animation by viewers and creators, and a workflow to bring the personalities to the forefront in the piece. Three distinct characters will be animated with the same set of animations to showcase how a personality can shine.

This thesis presents a prototype system for generating animation control systems for isosurface-based characters that blurs the distinction between a skeletal rig and a particle system. Managing articulation and deformation set-up can be challenging for amorphous characters whose surface shape is defined at render time and can only be viewed as an approximation during the process of defining an animation performance. This prototype system utilizes conventional scripted techniques for defining animation control systems integrated with a graphical user interface that provides art directable control over surface contour, shape and silhouette for isosurface-based characters. Once animated, these characters can be rendered using Rendermans RIBlobby implementation and provide visual feedback of fluid motion tests. The prototype system fits naturally within common practices in digital character setup and provides the animator control over isosurface-based characters.

<p>Motion capture is the process of capturing movements from real life into a computer. Existing motion capture systems are often very expensive and require advanced hardware that makes the process complex. This thesis will answer the following question: is it possible to create an optical motion capture system using only a single low cost Dvcamera (Digital Video Camera), that still will produce accurate motion capture data? To answer this question and construct our motion capture system we need to complete these following steps:</p><p>• Create a usable film sequence.</p><p>• Analyze the sequence.</p><p>• Create motion capture data.</p><p>• Apply the motion capture data for 3D character and analyze the outcome.</p><p>The method chosen for this thesis is constructive research. In short terms it is the study of whether we can or cannot build a new artifact. The following theoretic tools were used in the process of creating a motion capture system: Color theory, RGB, Connected component labeling, Skeletons in 3D animation, Calculating angels using trigonometry, .x files and Quaternions. We have found that an optical motion capture system is very complex and it is hard to produce as a low budget system. Our attempt did not live up to our expectations. The idea with using only one DV camera was to simplify the system since it would require no calibration or syncronisation. It would also make the system cost efficient and more available to the general public. The single camera solution unfortunatly created a number of problems in our system. Our system does however work with less complex movements. It can produce motion capture data that is accurate enough to be used in low budget games. It is also cost effective compared to other systems on the market. The system has a very easy setup and does not need any calibration in addition to the init position.</p>

Background. In recent years the use of Artifical Neural Networks(ANN) to generate character animations have expanded rapidly. However comparisons of the perceived realism and quality of these new approaches when compared to the results of a traditional pipeline have been lacking or non-existent. Objectives. This thesis aims to show initial data on whether the visual quality of one of these novel approaches is perceived to be of higher visual quality than similar keyframe-based approaches and if so why. As such the objectives of this thesis is to produce animations as a base of comparison for the method. Method. Keyframe animations performing similar actions to an ANN-based plug-in for the game engine unity were handcrafted after which a questionnaire study was performed. This study was sent to willing participants both experienced and inexperienced to gauge public opinion. Results. The results indicated that participants considered the ANN-based plug-in animations to overall be more realistic, natural, smooth and appealing. Statistical analysis via t-test shows a high statistical significance when comparing opinions on quality between the two sets. Conclusions. The ANN-based approach was considered by participants to be of superior visual quality due to reasons stated above. All experienced participants correctly guessed which of the two animation sets were AI-based and 1/3 of the inexperienced. However the inexperienced participants who guessed wrong stated similar motivations for their guess to those who guessed right. Which could imply an uncertainty about the capabilities of AI in the public consciousness that wasn’t accounted for in this thesis.

This thesis is a supporting paper for three of my 3D animations created and presented for a Master of Fine Arts graduate exhibition. It discusses how the two realms of graphic design and 3D animation helped me to develop my heuristic techniques of creating animations. Using the three animations as examples, I make an attempt to explain how linear and figurative images influence each other in the creative process of creature/character development. I also discuss the various influences and cumulative explorations behind the imageries of animation. A brief discussion about Asian Indian aesthetic concepts and the general methodology of creating 3D animation using Alias|Wavefront Maya is also included.

Yes / Physics-based skin deformation methods can greatly improve the realism of character animation, but require non-trivial training, intensive manual intervention, and heavy numerical calculations. Due to these limitations, it is generally time-consuming to implement them, and difficult to achieve a high runtime efficiency. In order to tackle the above limitations caused by numerical calculations of physics-based skin deformation, we propose a simple and efficient analytical approach for physics-based skin deformations. Specifically, we (1) employ Fourier series to convert 3D mesh models into continuous parametric representations through a conversion algorithm, which largely reduces data size and computing time but still keeps high realism, (2) introduce a partial differential equation (PDE)-based skin deformation model and successfully obtain the first analytical solution to physics-based skin deformations which overcomes the limitations of numerical calculations. Our approach is easy to use, highly efficient, and capable to create physically realistic skin deformations. / This research is supported by the PDE-GIR project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No.778035), the National Natural Science Foundation of China (Grant No.51475394), and Innovate UK (Knowledge Transfer Partnerships KTP.010860). Shaojun Bian is also supported by Chinese Scholar Council. Xiaogang Jin is supported by the Key Research and Development Program of Zhejiang Province (No.2018C01090) and the National Natural Science Foundation of China (No.61732015).