OmnimatteRF: Robust Omnimatte with 3D Background Modeling

• URL: http://arxiv.org/abs/2309.07749v1
• Date: Thu, 14 Sep 2023 14:36:22 GMT
• Title: OmnimatteRF: Robust Omnimatte with 3D Background Modeling
• Authors: Geng Lin, Chen Gao, Jia-Bin Huang, Changil Kim, Yipeng Wang, Matthias Zwicker, Ayush Saraf
• Abstract summary: We propose a novel video matting method, OmnimatteRF, that combines dynamic 2D foreground layers and a 3D background model. The 2D layers preserve the details of the subjects, while the 3D background robustly reconstructs scenes in real-world videos.
• Score: 42.844343885602214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video matting has broad applications, from adding interesting effects to casually captured movies to assisting video production professionals. Matting with associated effects such as shadows and reflections has also attracted increasing research activity, and methods like Omnimatte have been proposed to separate dynamic foreground objects of interest into their own layers. However, prior works represent video backgrounds as 2D image layers, limiting their capacity to express more complicated scenes, thus hindering application to real-world videos. In this paper, we propose a novel video matting method, OmnimatteRF, that combines dynamic 2D foreground layers and a 3D background model. The 2D layers preserve the details of the subjects, while the 3D background robustly reconstructs scenes in real-world videos. Extensive experiments demonstrate that our method reconstructs scenes with better quality on various videos.

Related papers

• Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting [94.84688557937123]
  Video-3DGS is a 3D Gaussian Splatting (3DGS)-based video refiner designed to enhance temporal consistency in zero-shot video editors. Our approach utilizes a two-stage 3D Gaussian optimizing process tailored for editing dynamic monocular videos. It enhances video editing by ensuring temporal consistency across 58 dynamic monocular videos.
  arXiv  Detail & Related papers  (2024-06-04T17:57:37Z)
• Cinematic Behavior Transfer via NeRF-based Differentiable Filming [63.1622492808519]
  Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections. We first introduce a reverse filming behavior estimation technique. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment.
  arXiv  Detail & Related papers  (2023-11-29T15:56:58Z)
• 3D Cinemagraphy from a Single Image [73.09720823592092]
  We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion.
  arXiv  Detail & Related papers  (2023-03-10T06:08:23Z)
• Text-To-4D Dynamic Scene Generation [111.89517759596345]
  We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency. The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment.
  arXiv  Detail & Related papers  (2023-01-26T18:14:32Z)
• Action2video: Generating Videos of Human 3D Actions [31.665831044217363]
  We aim to tackle the interesting yet challenging problem of generating videos of diverse and natural human motions from prescribed action categories. Key issue lies in the ability to synthesize multiple distinct motion sequences that are realistic in their visual appearances. Action2motionally generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
  arXiv  Detail & Related papers  (2021-11-12T20:20:37Z)
• Layered Neural Atlases for Consistent Video Editing [37.69447642502351]
  We present a method that decomposes, or "unwraps", an input video into a set of layered 2D atlases. For each pixel in the video, our method estimates its corresponding 2D coordinate in each of the atlases. We design our atlases to be interpretable and semantic, which facilitates easy and intuitive editing in the atlas domain.
  arXiv  Detail & Related papers  (2021-09-23T14:58:59Z)
• Deep 3D Mask Volume for View Synthesis of Dynamic Scenes [49.45028543279115]
  We introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras.
  arXiv  Detail & Related papers  (2021-08-30T17:55:28Z)
• Unsupervised object-centric video generation and decomposition in 3D [36.08064849807464]
  We propose to model a video as the view seen while moving through a scene with multiple 3D objects and a 3D background. Our model is trained from monocular videos without any supervision, yet learns to generate coherent 3D scenes containing several moving objects.
  arXiv  Detail & Related papers  (2020-07-07T18:01:29Z)

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.