# High-fidelity 3D Human Digitization from Single 2K Resolution Images

arXiv: https://arxiv.org/abs/2303.15108 · HTML: https://mlsanity.com/paper/2303.15108/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 61 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 39)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (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)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction](https://mlsanity.com/paper/2007.03858/) (2020)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [ARCH++: Animation-Ready Clothed Human Reconstruction Revisited](https://mlsanity.com/paper/2108.07845/) (2021)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [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)
- [Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing](https://mlsanity.com/paper/2204.08906/) (2022)
- [HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling](https://mlsanity.com/paper/2204.13686/) (2022)
- [Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction](https://mlsanity.com/paper/2006.08072/) (2020)
- [Detailed, accurate, human shape estimation from clothed 3D scan sequences](https://mlsanity.com/paper/1703.04454/) (2017)
- [Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation](https://mlsanity.com/paper/1904.10506/) (2019)
- [SMPLpix: Neural Avatars from 3D Human Models](https://mlsanity.com/paper/2008.06872/) (2020)
- [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)
- [DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras](https://mlsanity.com/paper/2105.00261/) (2021)

## Built on by (most cited first, 30 of 70)

- [SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction](https://mlsanity.com/paper/2312.06704/) (2023)
- [SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion](https://mlsanity.com/paper/2311.15855/) (2023)
- [LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds](https://mlsanity.com/paper/2503.10625/) (2025)
- [IDOL: Instant Photorealistic 3D Human Creation from a Single Image](https://mlsanity.com/paper/2412.14963/) (2024)
- [MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures](https://mlsanity.com/paper/2312.02963/) (2023)
- [4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations](https://mlsanity.com/paper/2404.18630/) (2024)
- [HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors](https://mlsanity.com/paper/2406.12459/) (2024)
- [Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models](https://mlsanity.com/paper/2406.08475/) (2024)
- [AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation](https://mlsanity.com/paper/2306.09864/) (2023)
- [GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting](https://mlsanity.com/paper/2401.09720/) (2024)
- [AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction](https://mlsanity.com/paper/2412.02684/) (2024)
- [PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing](https://mlsanity.com/paper/2409.10141/) (2024)
- [GAS: Generative Avatar Synthesis from a Single Image](https://mlsanity.com/paper/2502.06957/) (2025)
- [Gen-3Diffusion: Realistic Image-to-3D Generation via 2D & 3D Diffusion Synergy](https://mlsanity.com/paper/2412.06698/) (2024)
- [Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail](https://mlsanity.com/paper/2403.12028/) (2024)
- [CanonicalFusion: Generating Drivable 3D Human Avatars from Multiple Images](https://mlsanity.com/paper/2407.04345/) (2024)
- [Grounding Language in Multi-Perspective Referential Communication](https://mlsanity.com/paper/2410.03959/) (2024)
- [SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets](https://mlsanity.com/paper/2504.06982/) (2025)
- [SOAP: Style-Omniscient Animatable Portraits](https://mlsanity.com/paper/2505.05022/) (2025)
- [DAViD: Data-efficient and Accurate Vision Models from Synthetic Data](https://mlsanity.com/paper/2507.15365/) (2025)
- [UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections](https://mlsanity.com/paper/2509.24817/) (2025)
- [MVHumanNet++: A Large-scale Dataset of Multi-view Daily Dressing Human Captures with Richer Annotations for 3D Human Digitization](https://mlsanity.com/paper/2505.01838/) (2025)
- [SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction](https://mlsanity.com/paper/2510.07723/) (2025)
- [Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images](https://mlsanity.com/paper/2311.02892/) (2023)
- [HumanGif: Single-View Human Diffusion with Generative Prior](https://mlsanity.com/paper/2502.12080/) (2025)
- [Joint2Human: High-quality 3D Human Generation via Compact Spherical Embedding of 3D Joints](https://mlsanity.com/paper/2312.08591/) (2023)
- [GeneMAN: Generalizable Single-Image 3D Human Reconstruction from Multi-Source Human Data](https://mlsanity.com/paper/2411.18624/) (2024)
- [LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D](https://mlsanity.com/paper/2506.13766/) (2025)
- [R2Human: Real-Time 3D Human Appearance Rendering from a Single Image](https://mlsanity.com/paper/2312.05826/) (2023)
- [Synthesizing Moving People with 3D Control](https://mlsanity.com/paper/2401.10889/) (2024)

## Abstract (arXiv)

High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The proposed method separately recovers the global shape of a human and its details. The low-resolution depth network predicts the global structure from a low-resolution image, and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution depth network merges the global 3D shape and the detailed structures to infer the high-resolution front and back side depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at https://github.com/SangHunHan92/2K2K. In addition, we also provide 2,050 3D human models, including texture maps, 3D joints, and SMPL parameters for research purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets.