Computer Vision-Driven Intelligent Animation Generation: Technological Evolution and Development Trends​

Kangwon National University，Chuncheon City  South Korea, 24341 Shiqi  Yu

Abstract:Intelligent animation generation driven by computer vision is based on technologies such as image recognition and motion capture, realizing the transformation of animation production from manual drawing to intelligence. Its technological evolution has gone through different stages, from traditional algorithms to the application of deep learning, continuously improving the efficiency and quality of animation generation. In the future, this field will continue to develop in directions such as real-time interaction adaptation and cross-modal collaboration, providing more possibilities for the animation industry.

Keywords:Computer Vision；Intelligent Animation Generation； Technological Evolution； Development Trends

Preface​：

With the advancement of digital technology, the animation industry has increasing requirements for production efficiency and expression effects. Computer vision technology provides a new technical path for animation generation by analyzing and processing visual information in images and videos. Supported by computer vision, intelligent animation generation has broken the limitations of traditional production processes and is becoming an important direction of technological innovation in the animation field, promoting profound changes in animation production models.​

1 Significance of Computer Vision-Driven Intelligent Animation Generation​

1.1 Improving the Efficiency of Animation Production Processes​

Traditional animation production relies on manual frame-by-frame drawing or keyframe adjustment. For complex actions and large-scale scenes, the production cycle is often long.Computer vision technology can capture and analyze real motion data to directly convert physical movements into key animation parameters. For example, pose estimation algorithms can extract the motion trajectories of human joint points and automatically generate character animation clips, reducing the workload of manual adjustments. This technical application can shorten the time cost of the action design link in animation production, allowing the production team to focus more energy on creative design.​

1.2 Optimizing the Authenticity of Animation Visual Expression​

The visual authenticity of animation works directly affects the audience’s immersive experience. Computer vision technology can analyze the light and shadow changes and object motion laws in the real world and convert them into constraints for animation generation. The extraction of scene depth information based on image segmentation technology can make the object occlusion relationships in animations conform to physical logic; the optimization of animation material rendering effects can be achieved through visual recognition of the reflective properties of real materials. This animation generation method based on real visual data can enhance the rationality of visual expression while maintaining artistic expressiveness.​

2 Technological Evolution of Computer Vision-Driven Intelligent Animation Generation​

2.1 The Keyframe Generation Stage Supported by Traditional Visual Algorithms​

In the early stage, the application of computer vision in animation generation mainly focused on keyframe interpolation as the core technical path. The SIFT feature point matching algorithm is used to identify the positions of key elements in images, and combined with optical flow methods to calculate pixel motion vectors between adjacent frames, realizing automatic frame interpolation between keyframes. This stage of technology relies on manual setting of keyframes, and visual algorithms mainly solve the problem of smoothness of inter-frame transitions. Although it can reduce some repetitive drawing work, for complex dynamic scenes, motion trajectory distortion is prone to occur, and the generation effect is limited by the quality of manually set keyframes .​

2.2 The End-to-End Generation Stage Integrated with Deep Learning​

The introduction of deep learning technology has changed the technical logic of animation generation. Image generation models based on convolutional neural networks can directly learn animation features from original visual data, realizing the conversion from text descriptions or simple sketches to complete animation clips. Generative adversarial networks improve the detail richness and style consistency of animation images through adversarial training between generators and discriminators. This stage of technology no longer relies on manual setting of key parameters, but automatically completes animation generation through data driving, and the ability to handle complex scenes has been significantly improved.​

3 Development Trends of Computer Vision-Driven Intelligent Animation Generation​

3.1 Optimization of Animation Generation in Real-Time Interaction Scenarios​

Current animation generation technology is relatively mature in offline production scenarios, but there are still problems such as response delays in real-time interaction scenarios. In the future, animation generation technology based on lightweight visual models will become a development direction. Through model compression and computational optimization, core algorithms such as pose estimation and action generation can run efficiently on mobile devices to meet the needs of real-time animation generation in scenarios such as virtual live broadcasts and interactive games. At the same time, combined with multi-modal input fusion technology, it can quickly respond to real-time instructions such as user voices and gestures, improving the smoothness of animation performance during interaction.​

3.2 Expansion of Animation Generation through Cross-Modal Content Collaboration​

Animation generation is developing from a single visual input to cross-modal content collaboration. The combination of computer vision technology with natural language processing and audio analysis technologies can generate corresponding animation storyboards from text plot descriptions and generate matching animation actions from audio rhythm extraction. For example, by identifying music melodies and beats and combining visual scene analysis, animation scene switching and character action changes that conform to music emotions can be automatically generated. This cross-modal collaboration capability will expand the application scenarios of animation generation, enabling animation production to integrate more conveniently with other content forms.​

Conclusion​：Computer vision-driven intelligent animation generation has shown significant value in improving production efficiency and optimizing visual performance. Its technology has evolved from keyframe-assisted generation with traditional algorithms to end-to-end automatic generation with deep learning, gradually breaking through the limitations of traditional production models. In the future, with the development of technologies such as real-time interaction optimization and cross-modal collaboration, this field will further lower the threshold of animation production and expand the application of animation in more practical scenarios. At the same time, technological development also needs to pay attention to issues such as the personalized expression of animation styles, find a balance between technological progress and artistic creation, and promote the development of intelligent animation generation in a direction more in line with actual needs.

References:​
【1】Jia Huoran. The Impact of Generative Artificial Intelligence (AIGC) on Animation Creation[J]. Journal of Suihua University, 2024, 44(11): 48-50.
【2】Zhang Rui, Jiao Xiaoqiong. Research on Realistic Animation Generation Technology Based on Kinect[J]. Automation & Instrumentation, 2019, (08): 204-207.