Research on Temporal Consistency Animation Video Generation Method Based on Grid Convolution

This paper proposes an animation video generation framework that integrates grid convolution and temporal constraint mechanisms to address the deficiencies of existing methods in terms of temporal consistency, spatial detail fidelity, and real-time performance. This framework achieves structured spatial feature extraction through a multi-scale grid convolution module, models inter-frame associations using a dual-branch temporal constraint module, and dynamically balance quality and efficiency with the aid of an adaptive generation optimization module. Experiments show that this method achieves excellent performance on multiple datasets: the average PSNR reaches 38.6 dB, the SSIM is 0.942, the FID is as low as 12.8, and the temporal consistency error is controlled within 0.08. Compared with mainstream methods, the timing error is reduced by more than 35%, the detail retention rate is increased by 28%, and the real-time frame rate for generating 1080P videos is stable at 24-27 FPS. It has demonstrated excellent robustness in tests such as complex action generation, long sequence generation, and cross-resolution adaptation, providing an efficient and stable technical path for the industrial application of high-quality animation videos.