# Adaptive Multiplane Image Generation from a Single Internet Picture

arXiv: https://arxiv.org/abs/2011.13317 · HTML: https://mlsanity.com/paper/2011.13317/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images](https://mlsanity.com/paper/2205.11733/) (2022)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [Tiled Multiplane Images for Practical 3D Photography](https://mlsanity.com/paper/2309.14291/) (2023)
- [Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction](https://mlsanity.com/paper/2303.05937/) (2023)
- [LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors](https://mlsanity.com/paper/2412.09597/) (2024)
- [ROSGS: Relightable Outdoor Scenes With Gaussian Splatting](https://mlsanity.com/paper/2509.11275/) (2025)
- [Learning multiplane images from single views with self-supervision](https://mlsanity.com/paper/2110.09380/) (2021)
- [FaDIV-Syn: Fast Depth-Independent View Synthesis using Soft Masks and Implicit Blending](https://mlsanity.com/paper/2106.13139/) (2021)
- [CPSL: Representing Volumetric Video via Content-Promoted Scene Layers](https://mlsanity.com/paper/2511.14927/) (2025)

## Abstract (arXiv)

In the last few years, several works have tackled the problem of novel view synthesis from stereo images or even from a single picture. However, previous methods are computationally expensive, specially for high-resolution images. In this paper, we address the problem of generating a multiplane image (MPI) from a single high-resolution picture. We present the adaptive-MPI representation, which allows rendering novel views with low computational requirements. To this end, we propose an adaptive slicing algorithm that produces an MPI with a variable number of image planes. We present a new lightweight CNN for depth estimation, which is learned by knowledge distillation from a larger network. Occluded regions in the adaptive-MPI are inpainted also by a lightweight CNN. We show that our method is capable of producing high-quality predictions with one order of magnitude less parameters compared to previous approaches. The robustness of our method is evidenced on challenging pictures from the Internet.