Summary of 360vfi: a Dataset and Benchmark For Omnidirectional Video Frame Interpolation, by Wenxuan Lu et al.
360VFI: A Dataset and Benchmark for Omnidirectional Video Frame Interpolation
by Wenxuan Lu, Mengshun Hu, Yansheng Qiu, Liang Liao, Zheng Wang
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to frame interpolation for omnidirectional videos, which are essential for head-mounted 360° displays and portable 360° cameras. The current methods for traditional videos do not apply to omnidirectional videos due to their unique characteristics. To address this challenge, the authors propose a pyramid distortion-sensitive feature extractor that utilizes the equirectangular projection (ERP) format as prior information. Additionally, they develop a decoder that employs an affine transformation to facilitate the synthesis of intermediate frames. The authors also create a benchmark dataset, 360VFI, to evaluate the effectiveness of their approach in various distortion conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve frame interpolation for omnidirectional videos. Currently, these videos have low frame rates and can cause visual fatigue. The authors introduce a new approach that uses a pyramid feature extractor and affine transformation decoder to create more realistic intermediate frames. They also create a benchmark dataset with different distortion conditions to test their method. |
Keywords
* Artificial intelligence * Decoder
