# Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction

arXiv: https://arxiv.org/abs/2006.08072 · HTML: https://mlsanity.com/paper/2006.08072/

## Key facts

- Cited by 112 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 30 of 112)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [Neural Rays for Occlusion-aware Image-based Rendering](https://mlsanity.com/paper/2107.13421/) (2021)
- [GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians](https://mlsanity.com/paper/2312.02134/) (2023)
- [ARCH++: Animation-Ready Clothed Human Reconstruction Revisited](https://mlsanity.com/paper/2108.07845/) (2021)
- [SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video](https://mlsanity.com/paper/2201.12792/) (2022)
- [ARAH: Animatable Volume Rendering of Articulated Human SDFs](https://mlsanity.com/paper/2210.10036/) (2022)
- [InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://mlsanity.com/paper/2212.10550/) (2022)
- [Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition](https://mlsanity.com/paper/2302.11566/) (2023)
- [Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing](https://mlsanity.com/paper/2204.08906/) (2022)
- [TeCH: Text-guided Reconstruction of Lifelike Clothed Humans](https://mlsanity.com/paper/2308.08545/) (2023)
- [SHERF: Generalizable Human NeRF from a Single Image](https://mlsanity.com/paper/2303.12791/) (2023)
- [MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images](https://mlsanity.com/paper/2106.11944/) (2021)
- [SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction](https://mlsanity.com/paper/2312.06704/) (2023)
- [gDNA: Towards Generative Detailed Neural Avatars](https://mlsanity.com/paper/2201.04123/) (2022)
- [High-fidelity 3D Human Digitization from Single 2K Resolution Images](https://mlsanity.com/paper/2303.15108/) (2023)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [Fast-SNARF: A Fast Deformer for Articulated Neural Fields](https://mlsanity.com/paper/2211.15601/) (2022)
- [HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors](https://mlsanity.com/paper/2406.12459/) (2024)
- [PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence](https://mlsanity.com/paper/2203.01754/) (2022)
- [Global-correlated 3D-decoupling Transformer for Clothed Avatar Reconstruction](https://mlsanity.com/paper/2309.13524/) (2023)
- [3D Clothed Human Reconstruction in the Wild](https://mlsanity.com/paper/2207.10053/) (2022)
- [Learning to Regress Bodies from Images using Differentiable Semantic Rendering](https://mlsanity.com/paper/2110.03480/) (2021)
- [StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision](https://mlsanity.com/paper/2104.05289/) (2021)
- [High-Fidelity Clothed Avatar Reconstruction from a Single Image](https://mlsanity.com/paper/2304.03903/) (2023)
- [Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction](https://mlsanity.com/paper/2108.08478/) (2021)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)

## Abstract (arXiv)

We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color image of a clothed person. Our method is based on a deep implicit function-based representation to learn latent voxel features using a structure-aware 3D U-Net, to constrain the model in two ways: first, to resolve feature ambiguities in query point encoding, second, to serve as a coarse human shape proxy to regularize the high-resolution mesh and encourage global shape regularity. We show that, by both encoding query points and constraining global shape using latent voxel features, the reconstruction we obtain for clothed human meshes exhibits less shape distortion and improved surface details compared to competing methods. We evaluate Geo-PIFu on a recent human mesh public dataset that is 10 × larger than the private commercial dataset used in PIFu and previous derivative work. On average, we exceed the state of the art by 42.7% reduction in Chamfer and Point-to-Surface Distances, and 19.4% reduction in normal estimation errors.