# Topology-Preserved Human Reconstruction with Details

arXiv: https://arxiv.org/abs/2106.06313 · HTML: https://mlsanity.com/paper/2106.06313/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 47 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 30 of 30)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018)
- [Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose](https://mlsanity.com/paper/2008.09047/) (2020)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [Monocular Total Capture: Posing Face, Body, and Hands in the Wild](https://mlsanity.com/paper/1812.01598/) (2018)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [BodyNet: Volumetric Inference of 3D Human Body Shapes](https://mlsanity.com/paper/1804.04875/) (2018)
- [Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction](https://mlsanity.com/paper/2007.11432/) (2020)
- [Detailed Human Avatars from Monocular Video](https://mlsanity.com/paper/1808.01338/) (2018)
- [DeepWrinkles: Accurate and Realistic Clothing Modeling](https://mlsanity.com/paper/1808.03417/) (2018)
- [SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements](https://mlsanity.com/paper/2104.07660/) (2021)
- [Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation](https://mlsanity.com/paper/1904.10506/) (2019)
- [SiCloPe: Silhouette-Based Clothed People](https://mlsanity.com/paper/1901.00049/) (2018)
- [Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images](https://mlsanity.com/paper/1908.00439/) (2019)
- [Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods](https://mlsanity.com/paper/2006.01423/) (2020)
- [A review of 3D human pose estimation algorithms for markerless motion capture](https://mlsanity.com/paper/2010.06449/) (2020)
- [A Neural Network for Detailed Human Depth Estimation from a Single Image](https://mlsanity.com/paper/1910.01275/) (2019)

## Abstract (arXiv)

It is challenging to directly estimate the human geometry from a single image due to the high diversity and complexity of body shapes with the various clothing styles. Most of model-based approaches are limited to predict the shape and pose of a minimally clothed body with over-smoothing surface. While capturing the fine detailed geometries, the model-free methods are lack of the fixed mesh topology. To address these issues, we propose a novel topology-preserved human reconstruction approach by bridging the gap between model-based and model-free human reconstruction. We present an end-to-end neural network that simultaneously predicts the pixel-aligned implicit surface and an explicit mesh model built by graph convolutional neural network. Experiments on DeepHuman and our collected dataset showed that our approach is effective. The code will be made publicly available.