{"ID":2887886,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00471","arxiv_id":"2508.00471","title":"Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.00471","url_pdf":"https://arxiv.org/pdf/2508.00471v1","authors":"[\"Yiwen Wang\",\"Xinning Chai\",\"Yuhong Zhang\",\"Zhengxue Cheng\",\"Jun Zhao\",\"Rong Xie\",\"Li Song\"]","published":"2025-08-01T09:47:35Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"eess.IV\"]","methods":"[\"Diffusion Model\"]","has_code":false}