# DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image

arXiv: https://arxiv.org/abs/1903.10153 · HTML: https://mlsanity.com/paper/1903.10153/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 20 of 20)

- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022)
- [Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2407.07532/) (2024)
- [I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling](https://mlsanity.com/paper/2102.03725/) (2021)
- [ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors](https://mlsanity.com/paper/2109.07001/) (2021)
- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [DensePose From WiFi](https://mlsanity.com/paper/2301.00250/) (2022)
- [A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception](https://mlsanity.com/paper/2303.14176/) (2023)
- [STAF: 3D Human Mesh Recovery from Video with Spatio-Temporal Alignment Fusion](https://mlsanity.com/paper/2401.01730/) (2024)
- [Collaborative Neural Rendering using Anime Character Sheets](https://mlsanity.com/paper/2207.05378/) (2022)
- [UV-Based 3D Hand-Object Reconstruction with Grasp Optimization](https://mlsanity.com/paper/2211.13429/) (2022)
- [Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/2110.11680/) (2021)
- [Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation](https://mlsanity.com/paper/2112.06389/) (2021)
- [KITRO: Refining Human Mesh by 2D Clues and Kinematic-tree Rotation](https://mlsanity.com/paper/2405.19833/) (2024)
- [Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2112.12917/) (2021)
- [Humans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery](https://mlsanity.com/paper/2407.00574/) (2024)
- [Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images](https://mlsanity.com/paper/2106.11536/) (2021)
- [Inferring Implicit 3D Representations from Human Figures on Pictorial Maps](https://mlsanity.com/paper/2209.02385/) (2022)
- [Multi-RoI Human Mesh Recovery with Camera Consistency and Contrastive Losses](https://mlsanity.com/paper/2402.02074/) (2024)

## Abstract (arXiv)

Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data. Previous methods addressing these issues typically rely on predicting intermediate results such as body part segmentation, 2D/3D joints, silhouette mask to decompose the problem into multiple sub-tasks in order to utilize more 2D labels. Most previous works incorporated parametric body shape model in their methods and predict parameters in low-dimensional space to represent human body. In this paper, we propose to directly regress the 3D human mesh from a single color image using Convolutional Neural Network(CNN). We use an efficient representation of 3D human shape and pose which can be predicted through an encoder-decoder neural network. The proposed method achieves state-of-the-art performance on several 3D human body datasets including Human3.6M, SURREAL and UP-3D with even faster running speed.