Real-time biped character stepping

PhD ThesisA rudimentary biped activity that is essential in interactive evirtual worlds, such as video-games and training simulations, is stepping. For example, stepping is fundamental in everyday terrestrial activities that include walking and balance recovery. Therefore an effective 3D stepping control algorithm that is computationally fast and easy to implement is extremely valuable and important to character animation research. This thesis focuses on generating real-time controllable stepping motions on-the-fly without key-framed data that are responsive and robust (e.g.,can remain upright and balanced under a variety of conditions, such as pushes and dynami- cally changing terrain). In our approach, we control the character’s direction and speed by means of varying the stepposition and duration. Our lightweight stepping model is used to create coordinated full-body motions, which produce directable steps to guide the character with specific goals (e.g., following a particular path while placing feet at viable locations). We also create protective steps in response to random disturbances (e.g., pushes). Whereby, the system automatically calculates where and when to place the foot to remedy the disruption. In conclusion, the inverted pendulum has a number of limitations that we address and resolve to produce an improved lightweight technique that provides better control and stability using approximate feature enhancements, for instance, ankle-torque and elongated-body

Data-driven techniques for animating virtual characters

One of the key goals of current research in data-driven computer animation is the synthesis of new motion sequences from existing motion data. This thesis presents three novel techniques for synthesising the motion of a virtual character from existing motion data and develops a framework of solutions to key character animation problems. The first motion synthesis technique presented is based on the character’s locomotion composition process. This technique examines the ability of synthesising a variety of character’s locomotion behaviours while easily specified constraints (footprints) are placed in the three-dimensional space. This is achieved by analysing existing motion data, and by assigning the locomotion behaviour transition process to transition graphs that are responsible for providing information about this process. However, virtual characters should also be able to animate according to different style variations. Therefore, a second technique to synthesise real-time style variations of character’s motion. A novel technique is developed that uses correlation between two different motion styles, and by assigning the motion synthesis process to a parameterised maximum a posteriori (MAP) framework retrieves the desire style content of the input motion in real-time, enhancing the realism of the new synthesised motion sequence. The third technique presents the ability to synthesise the motion of the character’s fingers either o↵-line or in real-time during the performance capture process. The advantage of both techniques is their ability to assign the motion searching process to motion features. The presented technique is able to estimate and synthesise a valid motion of the character’s fingers, enhancing the realism of the input motion. To conclude, this thesis demonstrates that these three novel techniques combine in to a framework that enables the realistic synthesis of virtual character movements, eliminating the post processing, as well as enabling fast synthesis of the required motion

Inertial Sensing for Human Motion Analysis: Processing, Technologies, and Applications

Human motion has always attracted significant interest and curiosity. In particular, the last two centuries have seen a fast and great development of innovative techniques and technologies for the scientific analysis of human motion. If initially this was mainly due to the large interest in biomedical fields, a growing number of other leading applications has kept this interest alive until today. These applications emerge, for instance, in sport, entertainment, and industrial contexts. The first motion capture systems, appeared along the nineteenth century, were typically based on optical technologies and their development was profoundly interlaced with the contemporary development of photography and cinematography. Since then, many other different technologies have been employed to develop new motion capture systems, such as (but not limited to) inertial, mechanical, magnetic, and acoustic. In particular, inertial motion capture systems, based on the use of inertial sensors (such as the accelerometer, which measures the acceleration, and the gyroscope, which measures angular velocity), are likely to replace the previous ones and become a standard technology. This is mainly favored by the recent great improvement in the large-scale development of accurate inertial sensors ever cheaper. When referring to inertial human motion analysis, several application areas are driving current research and development efforts. A tentative list may include, for instance, the following: clinical and home monitoring and/or rehabilitation; ambient assisted living; computer graphics and computer animation; gaming and virtual reality; sport training; pedestrian navigation; and robotics. Furthermore, human motion analysis often implies a transversal investigation of many aspects of human motion, at different levels of abstraction and at different detail depths. For instance, one may just be interested in recognizing and estimating the pose of a person as well as in identifying the activities and/or the gestures that he/she is performing. Furthermore, one may be just interested in analyzing a restricted part of the body rather than focusing on the full body. Due to this heterogeneity of topics and intents, this thesis does not focus on a specific application or method, but aims at investigating different aspects of inertial human motion analysis, by specifically discussing the corresponding data processing approaches and the involved technologies. Four research areas have been taken into account which correspond to four types of applications: arm posture recognition; activity classification; evaluation of functional motor tasks; and motion reconstruction. In particular, these applications have been chosen in order to cover topics with different levels of abstraction and different detail depths