Text-to-Video Generation Using AnimateDiff and Temporal Diffusion Models
Abstract
Text-to-video generation demands simultaneous perframe visual quality, inter-frame motion realism, and long-range temporal consistency that single-frame image diffusion cannot satisfy. This paper presents a systematic analysis and evaluation of AnimateDiff, a modular framework that extends pretrained image diffusion models with plug-in temporal attention modules to produce temporally coherent video without retraining the image generation backbone. Our contributions are threefold: (1) a detailed architectural analysis covering the training objective, temporal self-attention formulation, sinusoidal positional encoding, and multi-resolution placement with supporting ablation experiments; (2) comparative quantitative evaluation against four baselines using LPIPS, optical flow MSE, and CLIP semantic similarity; and (3) systematic identification of five failure modes with root cause analysis and a structured survey of six concrete future research directions. Results confirm a 55% LPIPS improvement over frame-independent generation, approaching full video diffusion performance at a fraction of the training cost while fully preserving the visual quality and stylistic flexibility of the pretrained backbone.
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