# Accurate 3D Body Shape Regression using Metric and Semantic Attributes

arXiv: https://arxiv.org/abs/2206.07036 · HTML: https://mlsanity.com/paper/2206.07036/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 74 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 30 of 46)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [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)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [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)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [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)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [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)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [Learning to Estimate 3D Human Pose and Shape from a Single Color Image](https://mlsanity.com/paper/1805.04092/) (2018)
- [Unite the People: Closing the Loop Between 3D and 2D Human Representations](https://mlsanity.com/paper/1701.02468/) (2017)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation](https://mlsanity.com/paper/1808.05942/) (2018)
- [Monocular Expressive Body Regression through Body-Driven Attention](https://mlsanity.com/paper/2008.09062/) (2020)
- [BodyNet: Volumetric Inference of 3D Human Body Shapes](https://mlsanity.com/paper/1804.04875/) (2018)
- [Collaborative Regression of Expressive Bodies using Moderation](https://mlsanity.com/paper/2105.05301/) (2021)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021)
- [Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation](https://mlsanity.com/paper/2004.03686/) (2020)

## Built on by (most cited first, 30 of 50)

- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [PromptHMR: Promptable Human Mesh Recovery](https://mlsanity.com/paper/2504.06397/) (2025)
- [LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds](https://mlsanity.com/paper/2503.10625/) (2025)
- [Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2407.07532/) (2024)
- [DECO: Dense Estimation of 3D Human-Scene Contact In The Wild](https://mlsanity.com/paper/2309.15273/) (2023)
- [Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction](https://mlsanity.com/paper/2303.13796/) (2023)
- [AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction](https://mlsanity.com/paper/2412.02684/) (2024)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [On the Benefits of 3D Pose and Tracking for Human Action Recognition](https://mlsanity.com/paper/2304.01199/) (2023)
- [From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans](https://mlsanity.com/paper/2509.06607/) (2025)
- [Look Ma, no markers: holistic performance capture without the hassle](https://mlsanity.com/paper/2410.11520/) (2024)
- [RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset](https://mlsanity.com/paper/2303.12564/) (2023)
- [PuzzleAvatar: Assembling 3D Avatars from Personal Albums](https://mlsanity.com/paper/2405.14869/) (2024)
- [AWOL: Analysis WithOut synthesis using Language](https://mlsanity.com/paper/2404.03042/) (2024)
- [ChatHuman: Chatting about 3D Humans with Tools](https://mlsanity.com/paper/2405.04533/) (2024)
- [Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions](https://mlsanity.com/paper/2504.03639/) (2025)
- [Semantify: Simplifying the Control of 3D Morphable Models using CLIP](https://mlsanity.com/paper/2308.07415/) (2023)
- [Learning Dense UV Completion for Human Mesh Recovery](https://mlsanity.com/paper/2307.11074/) (2023)
- [KBody: Towards general, robust, and aligned monocular whole-body estimation](https://mlsanity.com/paper/2304.11542/) (2023)
- [BodyShapeGPT: SMPL Body Shape Manipulation with LLMs](https://mlsanity.com/paper/2410.03556/) (2024)
- [LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D](https://mlsanity.com/paper/2506.13766/) (2025)
- [Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos](https://mlsanity.com/paper/2308.10334/) (2023)
- [Total Selfie: Generating Full-Body Selfies](https://mlsanity.com/paper/2308.14740/) (2023)
- [BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed](https://mlsanity.com/paper/2404.03183/) (2024)
- [Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images](https://mlsanity.com/paper/2409.18364/) (2024)
- [UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units](https://mlsanity.com/paper/2505.09393/) (2025)
- [ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation](https://mlsanity.com/paper/2403.01345/) (2024)
- [MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images](https://mlsanity.com/paper/2506.09919/) (2025)

## Abstract (arXiv)

While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of data. While humans can label 2D joints, and these constrain 3D pose, it is not so easy to "label" 3D body shape. Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements; (2) we collect linguistic shape attributes for a wide range of 3D body meshes and the model images. Taken together, these datasets provide sufficient constraints to infer dense 3D shape. We exploit the anthropometric measurements and linguistic shape attributes in several novel ways to train a neural network, called SHAPY, that regresses 3D human pose and shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that they either lack significant body shape variation, ground-truth shape, or clothing variation. Thus, we collect a new dataset for evaluating 3D human shape estimation, called HBW, containing photos of "Human Bodies in the Wild" for which we have ground-truth 3D body scans. On this new benchmark, SHAPY significantly outperforms state-of-the-art methods on the task of 3D body shape estimation. This is the first demonstration that 3D body shape regression from images can be trained from easy-to-obtain anthropometric measurements and linguistic shape attributes. Our model and data are available at: shapy.is.tue.mpg.de