Mononizing Binocular Videos
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Abstract (arXiv)
This paper presents the idea ofmono-nizingbinocular videos and a frame-work to effectively realize it. Mono-nize means we purposely convert abinocular video into a regular monocular video with the stereo informationimplicitly encoded in a visual but nearly-imperceptible form. Hence, wecan impartially distribute and show the mononized video as an ordinarymonocular video. Unlike ordinary monocular videos, we can restore from itthe original binocular video and show it on a stereoscopic display. To start,we formulate an encoding-and-decoding framework with the pyramidal de-formable fusion module to exploit long-range correspondences between theleft and right views, a quantization layer to suppress the restoring artifacts,and the compression noise simulation module to resist the compressionnoise introduced by modern video codecs. Our framework is self-supervised,as we articulate our objective function with loss terms defined on the input:a monocular term for creating the mononized video, an invertibility termfor restoring the original video, and a temporal term for frame-to-framecoherence. Further, we conducted extensive experiments to evaluate ourgenerated mononized videos and restored binocular videos for diverse typesof images and 3D movies. Quantitative results on both standard metrics anduser perception studies show the effectiveness of our method.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 1; 2025: 1.
9 papers.
- 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videoscited by 204
- 2023Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fieldscited by 143
- 2025GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priorscited by 37
- 2021IICNet: A Generic Framework for Reversible Image Conversioncited by 13
- 2022Scale-arbitrary Invertible Image Downscalingcited by 11
- 2021Embedding Novel Views in a Single JPEG Imagecited by 10
- 2023Learning based 2D Irregular Shape Packingcited by 5
- 2023Neural Packing: from Visual Sensing to Reinforcement Learningcited by 3
- 2022Point Set Self-Embeddingcited by 1
- 2025GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors1 Apr 2025
- 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos3 Sep 2024
- 2023Neural Packing: from Visual Sensing to Reinforcement Learning17 Oct 2023
- 2023Learning based 2D Irregular Shape Packing19 Sep 2023
- 2023Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields21 Jul 2023
- 2022Point Set Self-Embedding28 Feb 2022
- 2022Scale-arbitrary Invertible Image Downscaling29 Jan 2022
- 2021IICNet: A Generic Framework for Reversible Image Conversion9 Sep 2021
- 2021Embedding Novel Views in a Single JPEG Image30 Aug 2021