# A Neural Network for Detailed Human Depth Estimation from a Single Image

arXiv: https://arxiv.org/abs/1910.01275 · HTML: https://mlsanity.com/paper/1910.01275/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [High-fidelity 3D Human Digitization from Single 2K Resolution Images](https://mlsanity.com/paper/2303.15108/) (2023)
- [NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras](https://mlsanity.com/paper/2103.07700/) (2021)
- [Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images](https://mlsanity.com/paper/2203.15007/) (2022)
- [High-Fidelity Clothed Avatar Reconstruction from a Single Image](https://mlsanity.com/paper/2304.03903/) (2023)
- [HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling](https://mlsanity.com/paper/2303.02700/) (2023)
- [M3D-VTON: A Monocular-to-3D Virtual Try-On Network](https://mlsanity.com/paper/2108.05126/) (2021)
- [ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References](https://mlsanity.com/paper/2103.06747/) (2021)
- [Garment4D: Garment Reconstruction from Point Cloud Sequences](https://mlsanity.com/paper/2112.04159/) (2021)
- [XAGen: 3D Expressive Human Avatars Generation](https://mlsanity.com/paper/2311.13574/) (2023)
- [Detailed Avatar Recovery from Single Image](https://mlsanity.com/paper/2108.02931/) (2021)
- [Human Pose and Shape Estimation from Single Polarization Images](https://mlsanity.com/paper/2108.06834/) (2021)
- [Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos](https://mlsanity.com/paper/2103.03319/) (2021)
- [HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences](https://mlsanity.com/paper/2103.15573/) (2021)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images](https://mlsanity.com/paper/2311.02892/) (2023)
- [Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction](https://mlsanity.com/paper/2201.12170/) (2022)
- [PIFu for the Real World: A Self-supervised Framework to Reconstruct Dressed Human from Single-view Images](https://mlsanity.com/paper/2208.10769/) (2022)
- [Monocular Human Digitization via Implicit Re-projection Networks](https://mlsanity.com/paper/2205.06468/) (2022)
- [Depth Estimation using Weighted-loss and Transfer Learning](https://mlsanity.com/paper/2404.07686/) (2024)
- [Topology-Preserved Human Reconstruction with Details](https://mlsanity.com/paper/2106.06313/) (2021)

## Abstract (arXiv)

This paper presents a neural network to estimate a detailed depth map of the foreground human in a single RGB image. The result captures geometry details such as cloth wrinkles, which are important in visualization applications. To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively. We design a training strategy to ensure both base and detail shapes can be faithfully learned by the corresponding network branches. Furthermore, we introduce a novel network layer to fuse a rough depth map and surface normals to further improve the final result. Quantitative comparison with fused `ground truth' captured by real depth cameras and qualitative examples on unconstrained Internet images demonstrate the strength of the proposed method. The code is available at https://github.com/sfu-gruvi-3dv/deep_human.