# Deep disentangled representations for volumetric reconstruction

arXiv: https://arxiv.org/abs/1610.03777 · HTML: https://mlsanity.com/paper/1610.03777/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Human Pose Manipulation and Novel View Synthesis using Differentiable Rendering](https://mlsanity.com/paper/2111.12731/) (2021)
- [Novel View Synthesis of Humans using Differentiable Rendering](https://mlsanity.com/paper/2303.15880/) (2023)

## Abstract (arXiv)

We introduce a convolutional neural network for inferring a compact disentangled graphical description of objects from 2D images that can be used for volumetric reconstruction. The network comprises an encoder and a twin-tailed decoder. The encoder generates a disentangled graphics code. The first decoder generates a volume, and the second decoder reconstructs the input image using a novel training regime that allows the graphics code to learn a separate representation of the 3D object and a description of its lighting and pose conditions. We demonstrate this method by generating volumes and disentangled graphical descriptions from images and videos of faces and chairs.