Video Decomposition Prior: A Methodology to Decompose Videos into Layers

• URL: http://arxiv.org/abs/2412.04930v2
• Date: Mon, 09 Dec 2024 03:01:28 GMT
• Title: Video Decomposition Prior: A Methodology to Decompose Videos into Layers
• Authors: Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava,
• Abstract summary: This paper introduces a novel video decomposition prior VDP' framework which derives inspiration from professional video editing practices. VDP framework decomposes a video sequence into a set of multiple RGB layers and associated opacity levels. We address tasks such as video object segmentation, dehazing, and relighting.
• Score: 74.36790196133505
• License:
• Abstract: In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in tasks like video dehazing and relighting, where replicating identical motions and camera angles in both corrupted and ground truth sequences is complicated. Moreover, these conventional methodologies perform best when the test distribution closely mirrors the training distribution. Recognizing these challenges, this paper introduces a novel video decomposition prior `VDP' framework which derives inspiration from professional video editing practices. Our methodology does not mandate task-specific external data corpus collection, instead pivots to utilizing the motion and appearance of the input video. VDP framework decomposes a video sequence into a set of multiple RGB layers and associated opacity levels. These set of layers are then manipulated individually to obtain the desired results. We addresses tasks such as video object segmentation, dehazing, and relighting. Moreover, we introduce a novel logarithmic video decomposition formulation for video relighting tasks, setting a new benchmark over the existing methodologies. We observe the property of relighting emerge as we optimize for our novel relighting decomposition formulation. We evaluate our approach on standard video datasets like DAVIS, REVIDE, & SDSD and show qualitative results on a diverse array of internet videos. Project Page - https://www.cs.umd.edu/~gauravsh/video_decomposition/index.html for video results.

Related papers

• Agent-based Video Trimming [17.519404251018308]
  We introduce a novel task called Video Trimming (VT) VT focuses on detecting wasted footage, selecting valuable segments, and composing them into a final video with a coherent story. AVT received more favorable evaluations in user studies and demonstrated superior mAP and precision on the YouTube Highlights, TVSum, and our own dataset for the highlight detection task.
  arXiv  Detail & Related papers  (2024-12-12T17:59:28Z)
• VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising [45.349350685858276]
  Video restoration task aims to recover high-quality videos from low-quality observations. Video often faces different types of degradation, such as blur, low light, and noise. We propose an efficient end-to-end video transformer approach for the joint task of video deblurring, low-light enhancement, and denoising.
  arXiv  Detail & Related papers  (2024-01-26T10:27:56Z)
• Transform-Equivariant Consistency Learning for Temporal Sentence Grounding [66.10949751429781]
  We introduce a novel Equivariant Consistency Regulation Learning framework to learn more discriminative representations for each video. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted. In particular, we devise a self-supervised consistency loss module to enhance the completeness and smoothness of the augmented video.
  arXiv  Detail & Related papers  (2023-05-06T19:29:28Z)
• Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [76.35271072704384]
  Deep learning models perform poorly when applied to videos with rare scenes or objects. We tackle this problem from two different angles: algorithm and dataset. We show that the debiased representation can generalize better when transferred to other datasets and tasks.
  arXiv  Detail & Related papers  (2022-09-20T00:30:35Z)
• Deep Video Prior for Video Consistency and Propagation [58.250209011891904]
  We present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. We show that temporal consistency can be achieved by training a convolutional neural network on a video with Deep Video Prior.
  arXiv  Detail & Related papers  (2022-01-27T16:38:52Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)
• Blind Video Temporal Consistency via Deep Video Prior [61.062900556483164]
  We present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly. We show that temporal consistency can be achieved by training a convolutional network on a video with the Deep Video Prior.
  arXiv  Detail & Related papers  (2020-10-22T16:19:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.