We'll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback

2025-04-24

AI Summary

Text-to-video (T2V) generation suffers from semantic and temporal inconsistency when conditioned on long, complex textual prompts. Method: We propose a zero-training, neural-symbolic feedback-based video post-processing optimization framework. It uniquely integrates symbolic reasoning—namely, event-logic modeling and object-relation verification—with neural feature alignment—specifically, cross-modal prompt-video consistency assessment—to automatically parse formal video representations and localize/correct frame-level semantic and temporal errors. Contribution/Results: Without model retraining or fine-tuning, our method improves prompt alignment by nearly 40% across multiple state-of-the-art T2V models. It significantly enhances dynamic event logicality and spatiotemporal consistency among multiple objects, establishing a new paradigm for efficient, interpretable T2V post-optimization.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Current text-to-video (T2V) generation models are increasingly popular due to their ability to produce coherent videos from textual prompts. However, these models often struggle to generate semantically and temporally consistent videos when dealing with longer, more complex prompts involving multiple objects or sequential events. Additionally, the high computational costs associated with training or fine-tuning make direct improvements impractical. To overcome these limitations, we introduce (projectname), a novel zero-training video refinement pipeline that leverages neuro-symbolic feedback to automatically enhance video generation, achieving superior alignment with the prompts. Our approach first derives the neuro-symbolic feedback by analyzing a formal video representation and pinpoints semantically inconsistent events, objects, and their corresponding frames. This feedback then guides targeted edits to the original video. Extensive empirical evaluations on both open-source and proprietary T2V models demonstrate that (projectname) significantly enhances temporal and logical alignment across diverse prompts by almost $40%$.

Problem

• Improving semantic consistency in text-to-video generation
• Reducing computational costs for video refinement
• Enhancing temporal alignment in complex video prompts

Innovation

• Zero-training video refinement pipeline
• Neuro-symbolic feedback for consistency
• Targeted edits based on formal analysis