Figure 1: Examples from our test set of motions. The left two images are natural (motion capture data). The two images to the right are unnatural (badly edited and incompletely cleaned motion). Joints that are marked in red-yellow were detected as having unnatural motion. Frames for these images were selected by the method presented in [Assa et al. 2005]. In this paper, we investigate whether it is possible to develop a measure that quantifies the naturalness of human motion (as defined by a large database). Such a measure might prove useful in verifying that a motion editing operation had not destroyed the naturalness of a motion capture clip or that a synthetic motion transition was within the space of those seen in natural human motion. We explore the performance of mixture of Gaussians (MoG), hidden Markov models (HMM), and switching linear dynamic systems (SLDS) on this problem. We use each of these statistical models alone and as part of an ensemble of smaller statistical models. We also implement a Naive Bayes (NB) model for a baseline comparison. We test these techniques on motion capture data held out from a database, keyframed motions, edited motions, motions with noise added, and synthetic motion transitions. We present the results as receiver operating characteristic (ROC) curves and compare the results to the judgments made by subjects in a user study.

Figure 1: Two representative models that users can interactively manipulate within our deformation system. (a) (column 1:) A desk lamp connected by revolute joints, and its color-coded components. The lampshade is manipulated with the same handle trajectory for three cases: (column 2:) joint-unaware deformation has difficulty facing the lampshade backward because of immovable joints, and links are bent unnaturally(131 cells). (column 3:) joint-aware deformation with fully rigid links(6 cells). (column 4:) joint-aware deformation with two deformable links in the middle(76 cells). (b) An Aibo-like robot dog with a soft tail, a soft body, and two soft ears interactively posed to walk and stand up. (b) Complex mesh models of man-made objects often consist of multiple components connected by various types of joints. We propose a joint-aware deformation framework that supports the direct manipulation of an arbitrary mix of rigid and deformable components. First we apply slippable motion analysis to automatically detect multiple types of joint constraints that are implicit in model geometry. For single-component geometry or models with disconnected components, we support user-defined virtual joints. Then we integrate manipulation handle constraints, multiple components, joint constraints, joint limits, and deformation energies into a single volumetric-cell-based space deformation problem. An iterative, parallelized Gauss-Newton solver is used to solve the resulting nonlinear optimization. Interactive deformable manipulation is demonstrated on a variety of geometric models while automatically respecting their multi-component nature and the natural behavior of their joints.

Interactive entertainment has long been one of the driving factors behind architectural innovation, pushing the boundaries of computing to achieve ever more realistic virtual experiences. Future entertainment applications will feature robust physics modeling to enable on-the-fly content creation. However, application designers must provide at least 30 graphical frames per second to provide the illusion of visual continuity. This constraint directly impacts the physics engine, which must deliver the results of physical interactions in the virtual world at a fraction of this frame rate. With more sophisticated applications combining massive numbers of complex entities, the cost of robust physics simulation will easily exceed the capability of today’s most power machines. This work explores the characteristics of real-time physics simulation, and proposes a suite of future-thinking benchmarks stressing different situations that represent the demands of future interactive entertainment. With this suite, we then explore techniques to help meet these demands, including parallel execution, a fast estimation approach that self-regulates error, and a value prediction technique that is allowed to get “close enough ” to the real value. We demonstrate that parallel execution together with the proposed fast estimation approach can satisfy the demands of nearly all of the PhysicsBench suite.

Abstract not found

Our goal in this project was to implement and validate the results of recent work employing statistical techniques to automatically determine a “naturalness” measure of human motion data. Using a training set of motion capture data (that in effect embodies our definition of naturalness), we learn several models to represent this natural motion, and test them on a variety of hand-selected positive and negative examples. The models we consider are the Naive Bayes model, mixtures of Gaussians, and hidden Markov models. We restrict our study to walking motions, but nonetheless achieve convincing and meaningful results, which are illustrated using ROC curves. In addition, we mention shortcomings of the original paper, and provide a few suggestions for further work.

Virtual characters are much in demand for animated movies, games, and other applications. Rapid advances in performance capture and advanced rendering techniques have allowed the movie industry in particular to create characters that appear very human-like. However, with these new capabilities has come the realization that such characters are yet not quite “right.” One possible hypothesis is that these virtual humans fall into an “Uncanny Valley”, where the viewer’s emotional response is repulsion or rejection, rather than the empathy or emotional engagement that their creators had hoped for. To explore these issues, we created three animated vignettes of an arguing couple with detailed motion for the face, eyes, hair, and body. In a set of perceptual experiments, we explore the relative importance of different anomalies using two different methods: a questionnaire to determine the emotional response to the full-length vignettes, with and without facial motion and audio; and a 2AFC (two alternative forced choice) task to compare the performance of a virtual “actor ” in short clips (extracts from the vignettes) depicting a range of different facial and body anomalies. We found that the facial anomalies are particularly salient, even when very significant body animation anomalies are present.

Human motion plays a key role in the production of films, video games, virtual reality applications, and the control of humanoid robots. Unfortunately, it is hard to generate high quality human motion for character animation either manually or algorithmically. As a result, approaches based on motion capture data have become a central focus of character animation research in recent years. We observe three principal weaknesses in previous work using data-driven approaches for modelling human motion. First, basic balance behaviours and locomotion tasks are currently not well modelled. Second, the ability to produce high quality motion that is responsive to its environment is limited. Third, knowledge about human motor control is not well utilized. This thesis develops several techniques to generalize motion capture character animations to balance and respond. We focus on balance and locomotion tasks, with an emphasis on responding to disturbances, user interaction, and motor control integration. For this purpose, we investigate both kinematic and dynamic models. Kinematic models are intuitive and fast to construct, but have narrow generality, and thus require