SVI360: Spherical Video Interpolation

• URL: http://arxiv.org/abs/2607.11710v1
• Date: Mon, 13 Jul 2026 15:37:52 GMT
• Title: SVI360: Spherical Video Interpolation
• Abstract summary: This paper addresses the problem of omnidirectional video, which plays an essential role in applications such as virtual reality and immersive video enhancement.<n>Existing video methods are not well-suited for spherical videos, as they have difficulty handling severe distortions close to the poles.<n>We propose SVI360, a dual-branch framework that combines the image frame and its rotated view to deal with these distortions.
• Score: 5.168141947651225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper addresses the problem of omnidirectional video interpolation, which plays an essential role in applications such as virtual reality and immersive video enhancement. Existing video interpolation methods are not well-suited for spherical videos, as they have difficulty handling severe distortions close to the poles. To address this issue, we propose SVI360, a dual-branch framework that combines the image frame and its rotated orthogonal view to deal with these distortions. The core methodological aspect of the approach is to reinforce equivariance of the flow displacements between the original and orthogonal views to improve intermediate frame prediction. Experiments show that our method outperforms state-of-the-art approaches in interpolation quality while maintaining accurate optical flow in four different public benchmarks. Code and pre-trained models are available at: https://icb-vision-ai.github.io/video360_interpolation/

Related papers

• ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler [53.98558445900626]
  Current image-to-video diffusion models, while powerful in generating videos from a single frame, need adaptation for two-frame conditioned generation.<n>We introduce a novel, bidirectional sampling strategy to address these off-manifold issues without requiring extensive re-noising or fine-tuning.<n>Our method employs sequential sampling along both forward and backward paths, conditioned on the start and end frames, respectively, ensuring more coherent and on-manifold generation of intermediate frames.
  arXiv  Detail & Related papers  (2024-10-08T03:01:54Z)
• 360VFI: A Dataset and Benchmark for Omnidirectional Video Frame Interpolation [13.122586587748218]
  This paper introduces the benchmark dataset, 360VFI, for Omnidirectional Video Frame Interpolation. We present a practical implementation that introduces a distortion prior from omnidirectional video into the network to modulate distortions.
  arXiv  Detail & Related papers  (2024-07-19T06:50:24Z)
• Fast Full-frame Video Stabilization with Iterative Optimization [21.962533235492625]
  We propose an iterative optimization-based learning approach using synthetic datasets for video stabilization. We develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. We take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views.
  arXiv  Detail & Related papers  (2023-07-24T13:24:19Z)
• Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction [54.00007868515432]
  Existing methods face challenges in estimating the accurate correction field due to the uniform velocity assumption. We propose a geometry-based Quadratic Rolling Shutter (QRS) motion solver, which precisely estimates the high-order correction field of individual pixels. Our method surpasses the state-of-the-art by +4.98, +0.77, and +4.33 of PSNR on Carla-RS, Fastec-RS, and BS-RSC datasets, respectively.
  arXiv  Detail & Related papers  (2023-03-31T15:09:18Z)
• H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions [63.23985601478339]
  We propose a simple yet effective solution, H-VFI, to deal with large motions in video frame. H-VFI contributes a hierarchical video transformer to learn a deformable kernel in a coarse-to-fine strategy. The advantage of such a progressive approximation is that the large motion frame problem can be predicted into several relatively simpler sub-tasks.
  arXiv  Detail & Related papers  (2022-11-21T09:49:23Z)
• TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation [50.49396123016185]
  Video frame (VFI) aims to synthesize an intermediate frame between two consecutive frames. We propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI) Our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks.
  arXiv  Detail & Related papers  (2022-07-19T03:37:49Z)
• Video Frame Interpolation Based on Deformable Kernel Region [18.55904569126297]
  We propose a deformable convolution for video, which can break the fixed grid restrictions on the kernel region. Experiments are conducted on four datasets to demonstrate the superior performance of the proposed model.
  arXiv  Detail & Related papers  (2022-04-25T02:03:04Z)
• Unsupervised Video Interpolation by Learning Multilayered 2.5D Motion Fields [75.81417944207806]
  This paper presents a self-supervised approach to video frame learning that requires only a single video. We parameterize the video motions by solving an ordinary differentiable equation (ODE) defined on a time-varying motion field. This implicit neural representation learns the video as a space-time continuum, allowing frame-time continuum at any temporal resolution.
  arXiv  Detail & Related papers  (2022-04-21T06:17:05Z)
• FineNet: Frame Interpolation and Enhancement for Face Video Deblurring [18.49184807837449]
  The aim of this work is to deblur face videos. We propose a method that tackles this problem from two directions: (1) enhancing the blurry frames, and (2) treating the blurry frames as missing values and estimate them by objective. Experiments on three real and synthetically generated video datasets show that our method outperforms the previous state-of-the-art methods by a large margin in terms of both quantitative and qualitative results.
  arXiv  Detail & Related papers  (2021-03-01T09:47:16Z)
• FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation [97.99012124785177]
  FLAVR is a flexible and efficient architecture that uses 3D space-time convolutions to enable end-to-end learning and inference for video framesupervised. We demonstrate that FLAVR can serve as a useful self- pretext task for action recognition, optical flow estimation, and motion magnification.
  arXiv  Detail & Related papers  (2020-12-15T18:59:30Z)
• Video Frame Interpolation via Generalized Deformable Convolution [18.357839820102683]
  Video frame aims at synthesizing intermediate frames from nearby source frames while maintaining spatial and temporal consistencies. Existing deeplearning-based video frame methods can be divided into two categories: flow-based methods and kernel-based methods. A novel mechanism named generalized deformable convolution is proposed, which can effectively learn motion in a data-driven manner and freely select sampling points in space-time.
  arXiv  Detail & Related papers  (2020-08-24T20:00:39Z)

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.