AI Powered High Quality Text to Video Generation with Enhanced Temporal Consistency

• URL: http://arxiv.org/abs/2511.00107v1
• Date: Thu, 30 Oct 2025 18:46:59 GMT
• Title: AI Powered High Quality Text to Video Generation with Enhanced Temporal Consistency
• Authors: Piyushkumar Patel,
• Abstract summary: We present MOVAI, a novel hierarchical framework that integrates compositional scene understanding with temporal diffusion aware models for high fidelity text to video synthesis.<n>Experiments on standard benchmarks demonstrate that MOVAI state-of-the-art performance, improving video quality metrics by 15.3% in LPIPS, 12.7% in FVD, and 18.9% in user preference studies compared to existing methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text to video generation has emerged as a critical frontier in generative artificial intelligence, yet existing approaches struggle with maintaining temporal consistency, compositional understanding, and fine grained control over visual narratives. We present MOVAI (Multimodal Original Video AI), a novel hierarchical framework that integrates compositional scene understanding with temporal aware diffusion models for high fidelity text to video synthesis. Our approach introduces three key innovations: (1) a Compositional Scene Parser (CSP) that decomposes textual descriptions into hierarchical scene graphs with temporal annotations, (2) a Temporal-Spatial Attention Mechanism (TSAM) that ensures coherent motion dynamics across frames while preserving spatial details, and (3) a Progressive Video Refinement (PVR) module that iteratively enhances video quality through multi-scale temporal reasoning. Extensive experiments on standard benchmarks demonstrate that MOVAI achieves state-of-the-art performance, improving video quality metrics by 15.3% in LPIPS, 12.7% in FVD, and 18.9% in user preference studies compared to existing methods. Our framework shows particular strength in generating complex multi-object scenes with realistic temporal dynamics and fine-grained semantic control.

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