We’ll Fix it in Post: Improving Text-to-Video Generation with Neuro-Symbolic Feedback
Summary: arXiv:2504.17180v4 Announce Type: replace-cross
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 NeuS-E, 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.
Key Features of NeuS-E
The NeuS-E pipeline stands out due to several innovative features:
- Zero-Training Approach: Unlike traditional methods that require extensive training on large datasets, NeuS-E refines videos without additional training, making it highly accessible and efficient.
- Neuro-Symbolic Feedback: The integration of neuro-symbolic feedback allows for a deeper semantic understanding of the video content, enabling more precise edits where needed.
- Automated Video Refinement: NeuS-E automates the process of identifying and correcting inconsistencies, significantly reducing the time and effort required for manual editing.
- Enhanced Alignment: The method achieves nearly a 40% improvement in the alignment of generated videos with the original prompts, offering a more coherent viewing experience.
Impact on Text-to-Video Generation
The introduction of NeuS-E marks a significant advancement in the field of text-to-video generation. By addressing the limitations of existing models, it opens up new possibilities for creators and developers. The ability to produce higher-quality videos with fewer resources is particularly valuable in industries such as entertainment, education, and marketing, where engaging video content is crucial.
Furthermore, this approach has the potential to democratize video production, allowing smaller teams and individual creators to leverage advanced AI technologies without the need for extensive computational resources. As a result, we can expect to see a surge in creativity and innovation in video content creation.
Conclusion
NeuS-E represents a pioneering step towards overcoming the challenges faced in text-to-video generation. By utilizing neuro-symbolic feedback, it not only enhances the quality of generated videos but also simplifies the editing process. As the demand for high-quality video content continues to grow, solutions like NeuS-E will be instrumental in shaping the future of video generation technologies.
Future Work
Researchers and developers are encouraged to explore the applications of NeuS-E in various domains. Future work could involve refining the algorithm further, exploring additional use cases, or integrating it with other AI-driven tools to create a comprehensive video production suite.