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- W4387076227 abstract "We present a video decomposition method that facilitates layer-based editing of videos with spatiotemporally varying lighting and motion effects. Our neural model decomposes an input video into multiple layered representations, each comprising a 2D texture map, a mask for the original video, and a multiplicative residual characterizing the spatiotemporal variations in lighting conditions. A single edit on the texture maps can be propagated to the corresponding locations in the entire video frames while preserving other contents' consistencies. Our method efficiently learns the layer-based neural representations of a 1080p video in 25s per frame via coordinate hashing and allows real-time rendering of the edited result at 71 fps on a single GPU. Qualitatively, we run our method on various videos to show its effectiveness in generating high-quality editing effects. Quantitatively, we propose to adopt feature-tracking evaluation metrics for objectively assessing the consistency of video editing. Project page: https://lightbulb12294.github.io/hashing-nvd/" @default.
- W4387076227 created "2023-09-27" @default.
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- W4387076227 date "2023-09-25" @default.
- W4387076227 modified "2023-09-28" @default.
- W4387076227 title "Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time" @default.
- W4387076227 doi "https://doi.org/10.48550/arxiv.2309.14022" @default.
- W4387076227 hasPublicationYear "2023" @default.
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