Research On Visual Media Fusion Based 2D Digital Role Generation

In recent years, digital roles have been widely used in various applications such as virtual reality, video games, animation films and sport simulations. The generation of digital roles, which utilizes several visual media, including image, video, 3D model and motion capture data, is a cross research field of visual media fusion. In the computer animation world, the role is composed of the texture, shape and behavior, which are generally from these visual media. Dealing with the visual media fusion is becoming an emergent research topic in the fields of computer graphics, computer vision and image/signal processing.This paper defines the image, video, motion capture data and 3D model to be the visual media, which can be perceived by the human perception and be able to be edited and stored. Based on the definition, we propose a visual media fusion framework, which can be used to classify varied kinds of computer animation from the visual media fusion's point of view.This paper proposes a robust and anti-noise segmentation algorithm based on Laplacian of Gaussian edge detection to extract the data from the image precisely. In this algorithm, the standard deviationαis coordinated to extract the character's edge, and a convolution process is carried out to remove the noise and extract the complete edge for the characters. We utilize the fulfilling algorithm to extract the whole character.This paper proposes an adaptive 2D digital role fusion algorithm in the lower-dimensional space. It adopts the Isomap as a lower-dimensional reduction method. According to the adjacency matrix, an undirected graph can be built. Based on this undirected graph, a shortest path graph can be constructed, and then by using the Multidimensional scaling, a lower-dimensional embedding space is built. Based on this space, the cartoonists can specify two points and construct the shortest path between them. Those points along the path can generate the new 2D digital role animation.This paper proposes a controllable 2D digital role fusion algorithm based on constrained spreading activation network. Initially, the features' effects on similarity including Euclidean edge distance, Hausdorff edge distance, motion distance, Color histogram distance and Sub-region color distance are analyzed and a fuzzy approach is adopted to assign the weights for each feature. After calculating the similarity precisely, the constrained spreading activation network is constructed.This paper proposes an automatic 2D digital role generation framework based on the constrained spreading activation network. The new animation can be generated according to the path drawn in the background image. After calculating the vanishing line in the background, the new generated animation can be applied into the background according to the scaling factor. This framework is convenient and flexible for the users.