标题： 潜在扩散空间中的语义与时间整合用于高保真视频超分辨率 显示英文标题

标题： Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution

摘要： 最近在视频超分辨率（VSR）模型方面的进展展示了在增强低分辨率视频方面的显著成果。 然而，由于在充分控制生成过程方面存在限制，实现与低分辨率输入的高保真对齐，同时保持帧间的时序一致性仍然是一个重大挑战。 在本工作中，我们提出了语义和时序引导的视频超分辨率（SeTe-VSR），这是一种新的方法，在潜在扩散空间中结合了语义和时序-空域引导以解决这些挑战。 通过结合高层次的语义信息并整合空间和时间信息，我们的方法在恢复复杂细节和确保时序连贯性之间实现了无缝平衡。 我们的方法不仅保留了高真实感的视觉内容，还显著提高了保真度。 大量实验表明，SeTe-VSR在细节恢复和感知质量方面优于现有方法，突显了其在复杂视频超分辨率任务中的有效性。 显示英文摘要

摘要： Recent advancements in video super-resolution (VSR) models have demonstrated impressive results in enhancing low-resolution videos. However, due to limitations in adequately controlling the generation process, achieving high fidelity alignment with the low-resolution input while maintaining temporal consistency across frames remains a significant challenge. In this work, we propose Semantic and Temporal Guided Video Super-Resolution (SeTe-VSR), a novel approach that incorporates both semantic and temporal-spatio guidance in the latent diffusion space to address these challenges. By incorporating high-level semantic information and integrating spatial and temporal information, our approach achieves a seamless balance between recovering intricate details and ensuring temporal coherence. Our method not only preserves high-reality visual content but also significantly enhances fidelity. Extensive experiments demonstrate that SeTe-VSR outperforms existing methods in terms of detail recovery and perceptual quality, highlighting its effectiveness for complex video super-resolution tasks.