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

• URL: http://arxiv.org/abs/2508.00471v1
• Date: Fri, 01 Aug 2025 09:47:35 GMT
• Title: Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution
• Authors: Yiwen Wang, Xinning Chai, Yuhong Zhang, Zhengxue Cheng, Jun Zhao, Rong Xie, Li Song,
• Abstract summary: We propose Semantic and Temporal Guided Video Super-Resolution (SeTe-VSR)<n>Our approach achieves a seamless balance between recovering intricate details and ensuring temporal coherence.<n>Our method not only preserves high-reality visual content but also significantly enhances fidelity.
• Score: 20.151571582095468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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