English(EN) Following Motion for Sequential Modeling in Video Frame Interpolation

基于Mamba的新模型通过运动引导增强视频帧插值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员开发了用于视频帧插值（MGMVFI）的运动引导Mamba，这是一种新颖的选择性状态空间模型（SSM）的改编，旨在改进视频帧插值。MGMVFI利用运动引导序列化（MGS）为SSM创建运动自适应输入顺序，将状态更新与语义相关的标记对齐，以实现更一致的特征传播，尤其是在处理大运动或动态运动时。该模型还结合了上下文合成，利用周围空间上下文增强帧间特征合成，并采用轻量级细化块以降低计算成本实现细节重建。实验表明，MGMVFI在标准的视频帧插值基准测试中取得了最先进的性能，尤其是在处理复杂和动态运动序列方面。 AI

影响引入了一种使用状态空间模型进行视频帧插值的新颖方法，有望提高在动态运动序列上的性能。

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arXiv cs.CV TIER_1 English(EN) · Jaehyun Park, Nam Ik Cho · 2026-08-25 04:00

基于序列建模的视频帧插值方法研究

arXiv:2608.22861v1 Announce Type: new Abstract: State Space Models (SSMs) have surfaced as a promising architecture in Video Frame Interpolation (VFI), as they can capture long-range dependencies with linear computational complexity. However, their predefined scanning order limit…

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基于序列建模的视频帧插值方法研究

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