Research and Analysis on Core Bottlenecks and Optimization Directions in Animated Image Generation Technology

This study focuses on three core issues to be addressed in the technology of generating animated images—improving the inter-frame coherence of long-sequence animations, achieving accurate text or pose-driven generation, and balancing generation efficiency with computational costs while ensuring quality. Although this technology has continuously promoted the improvement of animation quality through constant upgrades and optimizations, it has failed to solve the industry pain points of traditional animation production, such as long production cycles and high labor and time costs. Meanwhile, the demand for efficient and high-quality dynamic content is becoming increasingly urgent in fields like film and television special effects, game development, and virtual human-computer interaction. Currently, mainstream technologies have formed four core approaches: Generative Adversarial Networks can generate high-definition animation frames, with further improved dynamic coherence after subsequent optimizations; diffusion models possess the advantages of lightweight design and strong controllability, which can alleviate the disconnection between image details and dynamic effects; Transformers, relying on self-attention mechanisms, can effectively enhance coherence in long-sequence generation; hybrid technical schemes, through the combined use of different models, have achieved a balance between generation quality and efficiency.