# View Extrapolation of Human Body from a Single Image

arXiv: https://arxiv.org/abs/1804.04213 · HTML: https://mlsanity.com/paper/1804.04213/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset](https://mlsanity.com/paper/2203.14057/) (2022)
- [Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models](https://mlsanity.com/paper/2310.03020/) (2023)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation](https://mlsanity.com/paper/2306.09864/) (2023)
- [Layout-Guided Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2103.17022/) (2021)
- [Detailed Avatar Recovery from Single Image](https://mlsanity.com/paper/2108.02931/) (2021)
- [RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs](https://mlsanity.com/paper/2302.05486/) (2023)
- [Sketch2PoseNet: Efficient and Generalized Sketch to 3D Human Pose Prediction](https://mlsanity.com/paper/2510.26196/) (2025)
- [Robust 2D/3D Vehicle Parsing in CVIS](https://mlsanity.com/paper/2103.06432/) (2021)
- [Novel View Synthesis from a Single Image via Unsupervised learning](https://mlsanity.com/paper/2110.15569/) (2021)
- [Depth self-supervision for single image novel view synthesis](https://mlsanity.com/paper/2308.14108/) (2023)

## Abstract (arXiv)

We study how to synthesize novel views of human body from a single image. Though recent deep learning based methods work well for rigid objects, they often fail on objects with large articulation, like human bodies. The core step of existing methods is to fit a map from the observable views to novel views by CNNs; however, the rich articulation modes of human body make it rather challenging for CNNs to memorize and interpolate the data well. To address the problem, we propose a novel deep learning based pipeline that explicitly estimates and leverages the geometry of the underlying human body. Our new pipeline is a composition of a shape estimation network and an image generation network, and at the interface a perspective transformation is applied to generate a forward flow for pixel value transportation. Our design is able to factor out the space of data variation and makes learning at each step much easier. Empirically, we show that the performance for pose-varying objects can be improved dramatically. Our method can also be applied on real data captured by 3D sensors, and the flow generated by our methods can be used for generating high quality results in higher resolution.