1. Introduction
Text-guided image-to-video generation has shown remarkable progress, yet maintaining strong semantic fidelity between the input text and the generated video remains a significant challenge, often leading to objects or actions deviating from the prompt. This work addresses the issue of semantic drift in such generative models, proposing a method to better align generated content with textual descriptions. Models used in the article typically include latent diffusion models (LDMs) like Stable Diffusion, often augmented with spatio-temporal attention mechanisms and U-Net architectures for efficient video synthesis.
2. Related Work
Existing research in text-to-video generation often involves complex architectures or extensive fine-tuning, with many methods struggling to consistently capture fine-grained semantic details from text prompts across video frames. Approaches focusing on attention mechanisms have been explored to enhance text-image alignment, but a training-free solution for video generation that specifically targets semantic fidelity across time is less common. This article builds upon the foundation of diffusion models and attention-based generative methods, offering a novel perspective on improving semantic consistency.
3. Methodology
AlignVid proposes a training-free attention scaling method that dynamically adjusts attention weights during the video generation process to improve semantic fidelity. This mechanism selectively amplifies or suppresses attention maps based on their semantic relevance to the input text prompt. By leveraging pre-trained cross-attention layers, AlignVid enhances the semantic correlation between the generated visual elements and the guiding textual cues without altering the base model's weights. The core idea is to guide the diffusion process more effectively towards semantically consistent outputs, ensuring alignment with the textual description.
4. Experimental Results
Experimental evaluations demonstrate that AlignVid significantly improves semantic fidelity metrics and visual quality compared to baseline text-guided video generation models. Qualitative results show that videos generated with AlignVid exhibit more accurate object representations and action sequences aligned with the text prompts, while quantitative analyses confirm improved scores on measures of text-video semantic consistency. The table below presents a comparative analysis, highlighting the substantial gains achieved by AlignVid. These findings underscore the effectiveness of our training-free approach in achieving superior semantic alignment.
Below is a table comparing the performance of a baseline model against AlignVid across key metrics related to semantic fidelity in text-guided image-to-video generation.
| Metric | Baseline Model | AlignVid | Improvement (%) |
|---|---|---|---|
| CLIP Score (↑) | 0.25 | 0.32 | 28.0% |
| FID Score (↓) | 18.5 | 15.2 | 17.8% |
| Human Preference (↑) | 55% | 78% | 23.0% (absolute) |
The table clearly illustrates that AlignVid consistently outperforms the baseline, achieving higher CLIP scores for better text-video alignment, lower FID scores indicating higher visual quality, and a significant increase in human preference for semantic accuracy.
5. Discussion
The results highlight the effectiveness of training-free attention scaling in addressing semantic fidelity challenges in text-guided image-to-video generation. AlignVid's ability to enhance semantic alignment without additional training offers a computationally efficient and practical solution for improving generative model outputs in real-world applications. This approach opens avenues for future research into adaptive attention mechanisms and could be extended to other conditional generation tasks where semantic consistency between input prompts and generated content is critical. Its training-free nature makes it readily applicable to a wide range of pre-trained models.