World Class Tools Make Animation 3d Push Button Easy

Based on this, the identified and extracted video image frame feature scale is smaller and occupies less space. Using this, we deform the aligned meshes using linear blend skinning (LBS) to bring them all into the same canonical T-pose. The blend tree can be configured by the properties on the blend nodes. You can also add parts needed or remove parts of your story that doesn’t work well. At the same time, in order to prevent overlapping areas, 2 × 1 block is used as a block at the time of establishing a block, which also reduces the dimension, but when constructing a histogram, the gradient of each cell is divided into 9 parts. Both methods have their advantages and disadvantages in terms of time, cost and realism of the results. According to the experimental analysis of Dalal, if the area is reduced at this time, the feature dimension can be reduced, but the description ability is also reduced, and the computational complexity is increased. 

In the process of feature extraction, it highlights the main details of the animation video image, deepens the description of the feature, and effectively improves the recognition rate. Uses the similarity between evaluation features to realize dynamic recognition. PCANet uses the deep network learning process to extract the features of the animation video image, and the video image features extracted by PCANet and the features obtained by artificial rules are more abundant in detailed information and more prominent in texture structure, which provides rich prior knowledge for subsequent reconstruction and fills in the details of the animation video image of low-resolution film and television so as to facilitate the reconstruction of the animation video image of super-resolution film and television. PCANet uses learning to get multilayer network filters. For the multiframe animation video image reconstruction method, it is assumed that the high-resolution image and the low-resolution image are sparse expressions for their respective dictionaries, and then, the image sample features are obtained by PCANet depth network. Figures 3(a) and 4(a) are the reference film and television animation video images, Figures 3(b) and 4(b) are the reconstructions of film and television animation video images by using the visual communication design method of virtual reality environment animation character images, Figures 3(c) and 4(c) are the reconstructions of film and television animation video images by using the graphic beautification technology-based graphic visual communication design method, and Figures 3(d) and 4(d) are the reconstructions of this method.

Analysis of Table 2 shows that the length of time of the three methods used in 3D visual communication is different. Analysis of Table 3 shows that the size of the animation video image after the three methods is quite different. The size of the synthesized image that is determined by the proposed method is smaller than other methods. Compute the matrix into blocks for all samples in the data set, and select a sliding window of size , (normally, the pixel square window of the film and television animation video image used is 3, 5, or 7). After feature extraction for all the film and television animation video images is carried out through the aforesaid sliding window, the new data matrix of column can be obtained, and each column of the matrix represents a film and television animation video image block with a total of elements. Thus, achieve the effect of dimensionality reduction, and effectively complete the film and television animation video image enhancement design.

After obtaining the dictionary pair , the reconstruction of LR film and television animation video image and HR film and television animation video image can be obtained by using the high-resolution film and television animation video image reconstruction method based on sparse regular model. The formula is shown in the following equation:where represents grayscale, represents detail, and represents the normalization function of motion feature image and represents the feature value of film and television animation video image frame extracted at last. In the above formula, represents the -th film and television animation video image block in the film and television animation video image, represents the searched film and television animation video image block similar to , represents the attenuation factor, and represents the normalized value. As shown in Figure 5, the image peak signal-to-noise ratio of the present method is 31.2dB-40.9 dB, which is higher than the original image, proving that this method meets the practical needs of high-resolution image reconstruction. If we use Gaussian fuzzy model, set the Gaussian filter of 3×3 region invariant, set its sampling factor to 4, add Gaussian noise to all low-resolution images, and satisfy the signal-to-noise ratio of 30 dB. Finally, the problem of frame feature extraction of film and television animation video image evolves into the problem of foreground and background classification of film and television animation video image, and the separation coefficient is determined by the ratio function of the variance of feature distribution of film and television animation video image in the foreground and background region.