Research on Virtual Interactive Animation Design System Based on Deep Learning.

Area of Science:

• Computer Science
• Virtual Reality
• Artificial Intelligence

Background:

• Virtual reality (VR) technology offers significant advantages in instant messaging and communication networks.
• VR enhances resource utilization, device redundancy, immersion, interactivity, conceptualization, and holography.
• Animation data style recognition is a complex task in computer graphics and AI.

Purpose of the Study:

• To develop a semisupervised spatio-temporal feature model for recognizing animation data styles.
• To leverage Restricted Boltzmann Machines (RBMs) for enhanced feature perception in animation data.
• To improve the efficiency of animation design processes.

Main Methods:

• Utilized Restricted Boltzmann Machine (RBM) theory to build a semisupervised spatio-temporal feature model.
• Pretrained the bottom layer of the model with extensive unlabeled animation data for robust feature perception.
• Employed a layer-by-layer training approach, combining unsupervised pretraining with supervised fine-tuning on labeled data.
• Implemented a retraining strategy for the bottom model when feature representation issues arise, preserving top-layer parameters.

Main Results:

• The semisupervised model demonstrated enhanced feature perception capabilities for animation data.
• The layer-by-layer training facilitated model parallelism and efficient retraining.
• Simulation experiments confirmed a reduction in animation design assistance time by 10 minutes compared to baseline methods.

Conclusions:

• The proposed semisupervised spatio-temporal feature model effectively recognizes animation data styles.
• The RBM-based approach offers a parallelizable and adaptable solution for animation data analysis.
• This method significantly improves efficiency in the animation design pipeline.