Figure 1: Animations synthesized by our motion interpolation in a 5D parametric space. One parameter changes the style of motion from rough to delicate as shown by the bar indicator. The other four parameters are the heights and widths of two successive steps of stairs for gait motions, and the 2D start and end locations of the box for lifting motions. None of the motions required post-cleaning of foot-or hand-sliding.
Figure 1: Animations synthesized by our motion interpolation in a 5D parametric space. One parameter changes the style of motion from rough to delicate as shown by the bar indicator. The other four parameters are the heights and widths of two successive steps of stairs for gait motions, and the 2D start and end locations of the box for lifting motions. None of the motions required post-cleaning of foot-or hand-sliding. AbstractA common motion interpolation technique for realistic human animation is to blend similar motion samples with weighting functions whose parameters are embedded in an abstract space. Existing methods, however, are insensitive to statistical properties, such as correlations between motions. In addition, they lack the capability to quantitatively evaluate the reliability of synthesized motions. This paper proposes a method that treats motion interpolations as statistical predictions of missing data in an arbitrarily definable parametric space. A practical technique of geostatistics, called universal kriging, is then introduced for statistically estimating the correlations between the dissimilarity of motions and the distance in the parametric space. Our method statistically optimizes interpolation kernels for given parameters at each frame, using a pose distance metric to efficiently analyze the correlation. Motions are accurately predicted for the spatial constraints represented in the parametric space, and they therefore have few undesirable artifacts, if any. This property alleviates the problem of spatial inconsistencies, such as foot-sliding, that are associated with many existing methods. Moreover, numerical estimates for the reliability of predictions enable motions to be adaptively sampled. Since the interpolation kernels are computed with a linear system in real-time, motions can be interactively edited using various spatial controls.
This paper proposes a psychological model for simulating pedestrian behaviors in a crowded space. Our decision-making scheme controls plausible avoidance behavior depending on the positional relations among surrounding persons, on the basis of a two-stage personal space and a virtual memory structure as proposed in social psychology. Our system determines pedestrian walking speed with the crowd density to imitate the measured data in urban engineering, and automatically generates plausible motions of the individual pedestrian by composing a locomotion graph with motion capture data. Our approach based on psychology and a variety of actual measurements can increase the accuracy of simulation at both the micro and macro levels.
This paper presents an approach to image coding that first paints a regularly arranged dotted pattern, using colors picked from a texture sample with features corresponding to the embedded data. It then camouflages the dotted pattern using the same texture sample while preserving quality comparable to that of existing synthesis techniques.
Dynamic skin deformation is vital for creating life-like characters, and its real-time computation is in great demand in interactive applications. We propose a practical method to synthesize plausible and dynamic skin deformation based on a helper bone rig. This method builds helper bone controllers for the deformations caused not only by skeleton poses but also secondary dynamics effects. We introduce a state-space model for a discrete time linear time-invariant system that efficiently maps the skeleton motion to the dynamic movement of the helper bones. Optimal transfer of nonlinear, complicated deformations, including the effect of soft-tissue dynamics, is obtained by learning the training sequence consisting of skeleton motions and corresponding skin deformations. Our approximation method for a dynamics model is highly accurate and efficient owing to its low-rank property obtained by a sparsity-oriented nuclear norm optimization. The resulting linear model is simple enough to easily implement in the existing workflows and graphics pipelines. We demonstrate the superior performance of our method compared to conventional dynamic skinning in terms of computational efficiency including LOD controls, stability in interactive controls, and flexible expression in deformations.
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