LiveView: Dynamic Target-Centered MPI for View Synthesis
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Abstract (arXiv)
Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass. These methods produce fast, high-quality rendering of novel views, but rely on slow and computationally expensive MPI generation methods unsuitable for real-time applications. In addition, most MPI techniques use fixed depth/disparity planes which cannot be modified once the training is complete, hence offering very little flexibility at run-time. We propose LiveView - a novel MPI generation and rendering technique that produces high-quality view synthesis in real-time. Our method can also offer the flexibility to select scene-dependent MPI planes (number of planes and spacing between them) at run-time. LiveView first warps input images to target view (target-centered) and then learns to generate a target view centered MPI, one depth plane at a time (dynamically). The method generates high-quality renderings, while also enabling fast MPI generation and novel view synthesis. As a result, LiveView enables real-time view synthesis applications where an MPI needs to be updated frequently based on a video stream of input views. We demonstrate that LiveView improves the quality of view synthesis while being 70 times faster at run-time compared to state-of-the-art MPI-based methods.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 11 Jul 2021.
Builds on
10 of 32 references.
References · 10
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
- 2020Neural Sparse Voxel Fieldscited by 959
- 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
- 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
- 2020Single-View View Synthesis with Multiplane Imagescited by 263
- 2019DeepView: View Synthesis with Learned Gradient Descentcited by 253
- 2019Pushing the Boundaries of View Extrapolation with Multiplane Imagescited by 191
- 2015DeepStereo: Learning to Predict New Views from the World's Imagerycited by 185
- 2016Learning-Based View Synthesis for Light Field Camerascited by 145
Built on by
Citing papers per year: 2022: 1; 2023: 3.
4 papers.
- 2022Generative Multiplane Images: Making a 2D GAN 3D-Awarecited by 57
- 2023Tiled Multiplane Images for Practical 3D Photographycited by 14
- 2023Generative Multiplane Neural Radiance for 3D-Aware Image Generationcited by 2
- 2023fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representationscited by 1
- 2023fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations26 Dec 2023
- 2023Tiled Multiplane Images for Practical 3D Photography25 Sep 2023
- 2023Generative Multiplane Neural Radiance for 3D-Aware Image Generation3 Apr 2023
- 2022Generative Multiplane Images: Making a 2D GAN 3D-Aware21 Jul 2022