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Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis
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2026-01-27 17:18:26
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Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis ECCV 2022 Long Zhuo 1 , Guangcong Wang 2 , Shikai Li 3 , Wayne Wu 1,3 , Ziwei Liu 2 ✉ 1 Shanghai AI Laboratory, 2 S-Lab, Nanyang Technological University, 3 SenseTime Research Arxiv Video GitHub Abstract Video-to-Video synthesis (Vid2Vid) has achieved remarkable results in generating a photo-realistic video from a sequence of semantic maps. However, this pipeline suffers from high computational cost and long inference latency, which largely depends on two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly compressed via more efficient network architectures. Nevertheless, existing methods mainly focus on slimming network architectures and ignore the size of the sequential data str...
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