This paper describes a method for creating cel-style CG animations of waving hair. In this method, gatherings of air are considered as virtual circles moving at a constant velocity, and hair bundles are modeled as elastic bodies. Deformation of the hair bundles is then calculated by simulating collision events between the virtual circles and the hair bundles. Since the method is based on the animator's technique used in creation of the traditional cel animations, it is expected to suppress a feeling of strangeness that is often introduced by the conventional procedural animation techniques.
We previously proposed a machine learning based post filtering method for reducing image artifacts caused by lossy compression. The method classifies reconstructed image samples into three categories using a support vector machine (SVM) to roughly discriminate magnitude of the reconstruction errors. Then, an optimum offset value is added to the samples belonging to each category in a similar way to the post filtering technique called sample adaptive offset (SAO) used in the H.265/HEVC standard. In this paper, two kinds of SVM classifiers are adaptively switched according to information on block boundaries of transform units (TUs) in H.265/HEVC intra-frame coding. Furthermore, samples used for a feature vector, which will be fed to the SVM classifier, are rotated at the block boundary to properly capture local characteristics of the reconstruction errors.
This paper describes a novel lossless video coding method that directly estimates a probability distribution of image values pel-by-pel. In the estimation process, several examples, i.e. a set of pels whose neighborhoods are similar to a local texture of the target pel to be encoded, are gathered from search windows located on an already encoded area of the current frame as well as those of the previous frames. Then the probability distribution is modeled as a weighted sum of the Gaussian functions whose center positions are given by the individual examples. Furthermore, model parameters that control shapes of the Gaussian functions are numerically optimized so that the resulting coding rate can be a minimum. Simulation results indicate that the coding performance can be improved by increasing the number of reference frames.
In general, "drawing collapse" is a word used when very low quality animated contents are broadcast. For example, perspective of the scene is unnaturally distorted and/or sizes of people and buildings are abnormally unbalanced. In our research, possibility of automatic discrimination of drawing collapse is explored for the purpose of reducing a workload for content check typically done by the animation director. In this paper, we focus only on faces of animated characters as a preliminary task, and distances as well as angles between several feature points on facial parts are used as input data. By training a support vector machine (SVM) using the input data extracted from both positive and negative example images, about 90% of discrimination accuracy is obtained when the same character is tested.