Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time

• URL: http://arxiv.org/abs/2309.14022v1
• Date: Mon, 25 Sep 2023 10:36:14 GMT
• Title: Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time
• Authors: Cheng-Hung Chan, Cheng-Yang Yuan, Cheng Sun, and Hwann-Tzong Chen
• Abstract summary: We present a video decomposition method that facilitates layer-based editing of videos withtemporally varying lighting effects. Our method efficiently learns layer-based neural representations of a 1080p video in 25s per frame via coordinate hashing. We propose to adopt evaluation metrics for objectively assessing the consistency of video editing.
• Score: 14.015909536844337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/

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