# Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation

arXiv: https://arxiv.org/abs/1904.10506 · HTML: https://mlsanity.com/paper/1904.10506/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 30 of 71)

- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [Structured Local Radiance Fields for Human Avatar Modeling](https://mlsanity.com/paper/2203.14478/) (2022)
- [TeCH: Text-guided Reconstruction of Lifelike Clothed Humans](https://mlsanity.com/paper/2308.08545/) (2023)
- [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022)
- [SHERF: Generalizable Human NeRF from a Single Image](https://mlsanity.com/paper/2303.12791/) (2023)
- [SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction](https://mlsanity.com/paper/2312.06704/) (2023)
- [High-fidelity 3D Human Digitization from Single 2K Resolution Images](https://mlsanity.com/paper/2303.15108/) (2023)
- [DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras](https://mlsanity.com/paper/2105.00261/) (2021)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors](https://mlsanity.com/paper/2406.12459/) (2024)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras](https://mlsanity.com/paper/2207.08000/) (2022)
- [Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction](https://mlsanity.com/paper/2309.13524/) (2023)
- [AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation](https://mlsanity.com/paper/2306.09864/) (2023)
- [Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction](https://mlsanity.com/paper/2108.08478/) (2021)
- [AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction](https://mlsanity.com/paper/2412.02684/) (2024)
- [SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark](https://mlsanity.com/paper/2310.20436/) (2023)
- [FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction](https://mlsanity.com/paper/2206.02194/) (2022)
- [ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References](https://mlsanity.com/paper/2103.06747/) (2021)
- [JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction](https://mlsanity.com/paper/2204.10549/) (2022)
- [D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field](https://mlsanity.com/paper/2308.08857/) (2023)
- [AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture](https://mlsanity.com/paper/2207.02031/) (2022)
- [Garment4D: Garment Reconstruction from Point Cloud Sequences](https://mlsanity.com/paper/2112.04159/) (2021)
- [Template-Free Single-View 3D Human Digitalization with Diffusion-Guided LRM](https://mlsanity.com/paper/2401.12175/) (2024)

## Abstract (arXiv)

This paper presents a novel framework to recover detailed human body shapes from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric based template that lacks the surface details. As such the resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. We are able to restore detailed human body shapes beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance. The code is available in https://github.com/zhuhao-nju/hmd.git