In order to make the expression and action of animated virtual characters more realistic, this paper proposes a virtual character expression and action system based on 3D animation. The system hardware module is used to complete the collection and processing of image data and human bone data. Then, the positioning of human skeleton points and the mapping relationship between joint points and moving skeleton points are constructed, and finally the virtual character model is constructed. On this basis, this paper completes the correspondence of feature points through facial feature point mapping and completes the face model alignment by aligning the video face with the 3D animation virtual face. The Laplace coordinate recovery model is used to reconstruct the facial expression action, complete the simulation of 3D animation virtual character expression action and realize the design of the virtual character expression action system based on 3D animation. The experimental results show that in the aspect of expression movement, the system in this paper has a better effect than the real-time motion capture technology system and the 2D animation expression movement system with a 95.40% simulation degree. The fidelity of skin texture processing in this system is 97.60%. Conclusion. The designed system can effectively simulate the facial expression in the character image and integrate it into the three-dimensional animation to make the virtual character more vivid. After rendering and skin texture processing on the Unity3D platform of the system, the authenticity of the virtual character is enhanced.

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Task Image Setting of 3D Animation Based on Virtual Reality and Artificial Intelligence

Image matching is a basic problem in image processing and pattern recognition. It is used to calculate the visual similarity between images taken in the same scene with different sensors, different perspectives or at different times. In addition to image adjustment, it is an indispensable step in image analysis and digital photogrammetry. It is also important for applications such as automatic navigation, image processing, medical image analysis, and motion estimation. The current image adjustment technology can be divided into three categories: domain-based image conversion technology, gray-scale-based technology, and performance-based technology. Among them, the feature-based matching algorithm directly matches the features of the image, so it greatly improves the calculation efficiency and is easy to adapt to complex image transformations, such as geometric distortion, different resolutions, and image transformations at different angles. Image matching refers to the process of using effective matching algorithms to find the same or similar cue points for two or more image data. In applications such as medical image processing and analysis, remote sensing monitoring, weapon movement and image processing, image matching technology is an important step. Images have strong structural features, such as corners, edges, statistics, and textures. These functions play an important role in image matching and scanning technology. The key to many image matching problems depends on selection, detection and expression. For different image matching problems, different functions are selected, and the matching results may be very different.

Xiaobo Jiang

Papers

Research on feature point generation and matching method optimization in image matching algorithm