![]() ![]() The combined acceleration techniques in conjunction with sophisticated planning and control methodologies enable us to synthesize ever more realistic characters that go beyond pre-recorded ragdolls towards more self-driven problem solving avatars. While the development of highly parallel processors, such as the graphical processing unit (GPU), has opened the door to performance accelerated techniques allowing us to solve complex physical simulations in reasonable time frames. Instead character animations can be created using biologically inspired algorithms in conjunction with physics-based systems. For example, generating human movements without key-frame data. The emergence of evolving search techniques (e.g., genetic algorithms) has paved the way for innovative character animation solutions. The experimental results can demonstrate the efficient performance of Diffusion Score in feature selection. Diffusion Score can effectively improve the stability by minimizing large absolute errors and large relative errors of the features. Therefore, Laplacian Score is sensitive in feature selection. It makes the number of the nearest neighbor K in graph construction to be an insensitive parameter. The time scale of Markov process can incorporate the cluster structure of data at different levels of granularity. Since diffusion distance integrates “volume” of paths connecting data points, it is tolerant to noises. The diffusion distance sums over all paths' lengths which connect two data points. Specifically, the Markov process is carried out to find meaningful geometric descriptions of the whole cartoon dataset. This paper proposes a novel feature selection method named Diffusion Score which captures the geometrical properties of the data structure by preserving the diffusion distance. However, this measurement is sensitive to noise. Previous methods adopt pairwise distance to evaluate the similarity. The main issue in similarity estimation is choosing efficient features to describe cartoon images. Similarity estimation is critical for the computer-assisted cartoon animation system to improve the efficiency of cartoon generations. ![]()
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