Interactive Low-Dimensional Human Motion Synthesis by Combining Motion Models and PIK

This paper explores the issue of interactive low-dimensional human motion synthesis. We compare the performances of two motion models, i.e. Principal Components Analysis (PCA) or Probabilistic PCA (PPCA), for solving a constrained optimization problem within a low-dimensional latent space. We use PCA or PPCA as a first step of preprocessing to reduce the dimensionality of the database to make it tractable, and to encapsulate only the essential aspects of a specific motion pattern. Interactive user control is provided by formulating a low-dimensional optimization framework that uses a Prioritized Inverse Kinematics (PIK) strategy. The key insight of PIK is that the user can adjust a motion by adding constraints with different priorities. We demonstrate the robustness of our approach by synthesizing various styles of golf swing. This movement is challenging in the sense that it is highly coordinated and requires a great precision while moving with high speeds. Hence, any artifact is clearly noticeable in the solution movement. We simultaneously show results comparing local and global motion models regarding synthesis realism and performance. Finally, the quality of the synthesized animations is assessed by comparing our results against a per-frame PIK technique

Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control

Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control

Motion Modeling: Can We Get Rid of Motion Capture?

For situations like crowd simulation, serious games, and VR-based training, flexible and spontaneous movements are extremely important. Motion models would be the best strategy to adopt, but unfortunately, they are very costly to develop and the results are disappointing. Motion capture is still the most popular way. The ultimate in terms of motion models seems to be data-driven. Motion retargeting and PCA-based models are well used but they still rely strongly to Motion Capture. In this paper, we try to analyze the situation and illustrate it using a few case studies

Data-driven constraint-based motion editing

The growth of motion capture systems has contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the various captured motions normally require specific needs. Consequently, modifying and reusing these motions in new situations – for example, retargeting it to a new environment – became an increasing area of research known as motion editing. In the last few years, human motion editing has become one of the most active research areas in the field of computer animation. In this thesis, we introduce and discuss a novel method for interactive human motion editing. Our main contribution is the development of a Low-dimensional Prioritized Inverse Kinematics (LPIK) technique that handles user constraints within a low-dimensional motion space – also known as the latent space. Its major feature is to operate in the latent space instead of the joint space. By construction, it is sufficient to constrain a single frame with LPIK to obtain a natural movement enforcing the intrinsic motion flow. The LPIK has the advantage of reducing the size of the Jacobian matrix as the motion latent space dimension is small for a coordinated movement compared to the joint space. Moreover, the method offers the compelling advantage that it is well suited for characters with large number of degrees of freedom (DoFs). This is one of the limitations of IK methods that perform optimizations in the joint space. In addition, our method still provides faster deformations and more natural-looking motion results compared to goal-directed constraint-based methods found in the literature. Essentially, our technique is based on the mathematical connections between linear motion models such as Principal Component Analysis (PCA) and Prioritized Inverse Kinematics (PIK). We use PCA as a first stage of preprocessing to reduce the dimensionality of the database to make it tractable and to encapsulate an underlying motion pattern. And after, to bound IK solutions within the space of natural-looking motions. We use PIK to allow the user to manipulate constraints with different priorities while interactively editing an animation. Essentially, the priority strategy ensures that a higher priority task is not affected by other tasks of lower priority. Furthermore, two strategies to impose motion continuity based on PCA are introduced. We show a number of experiments used to evaluate and validate (both qualitatively and quantitatively) the benefits of our method. Finally, we assess the quality of the edited animations against a goal-directed constraint-based technique, to verify the robustness of our method regarding performance, simplicity and realism

Motion Pattern Encapsulation for Data-Driven Constraint-Based Motion Editing

The growth of motion capture systems have contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the captured motions normally attend specific needs. As an effort for adapting and reusing captured human motions in new tasks and environments and improving the animator’s work, we present and discuss a new data-driven constraint-based animation system for interactive human motion editing. This method offers the compelling advantage that it provides faster deformations and more natural-looking motion results compared to goal-directed constraint-based methods found in the literature

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