# Learning Body Shape and Pose from Dense Correspondences

## TL;DR

This paper introduces a novel method for learning 3D human body shape and pose from 2D images using dense correspondences, eliminating the need for 3D annotations or motion capture data.

## Contribution

It proposes a 'deform-and-learn' training strategy that leverages dense correspondences and deformable surface registration to learn 3D human models from 2D images without 3D labels.

## Key findings

- Successfully learns 3D human shape and pose from 2D images.
- Does not require 3D pose annotations or motion capture data.
- Achieves comparable results to methods using 3D supervision.

## Abstract

In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose). The idea is to use dense correspondences between image points and a body surface, which can be annotated on in-the wild 2D images, and extract and aggregate 3D information from them. To do so, we propose a training strategy called ``deform-and-learn" where we alternate deformable surface registration and training of deep convolutional neural networks (ConvNets). Unlike previous approaches, our method does not require 3D pose annotations from a motion capture (MoCap) system or human intervention to validate 3D pose annotations.