DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image
arXiv:1903.10153
Abstract
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.
arXiv comment10 pages, 6 figures
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Cited by in corpus (9)
- Recovering 3D Human Mesh from Monocular Images: A Survey
- PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images
- PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
- Semantics-Aligned Representation Learning for Person Re-identification
- BCNet: Learning Body and Cloth Shape from A Single Image
- Inferring Implicit 3D Representations from Human Figures on Pictorial Maps
- 3D Human Mesh Regression with Dense Correspondence
- Hierarchical Kinematic Human Mesh Recovery
- Deep3DPose: Realtime Reconstruction of Arbitrarily Posed Human Bodies from Single RGB Images