# Neural Scene Decomposition for Multi-Person Motion Capture

## TL;DR

This paper introduces a self-supervised neural scene decomposition method that enables 3D multi-person pose estimation from full-frame images without requiring extensive labeled data.

## Contribution

It presents a novel multi-layered neural scene decomposition approach that leverages multiview self-supervision for 3D pose estimation of multiple people.

## Key findings

- Effective multi-person 3D pose estimation without 2D/3D labels
- Works on full-frame images with multiple subjects
- Reduces need for annotated training data

## Abstract

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned features are a valuable starting point to learn from limited labeled data. However, when it comes to 3D motion capture of multiple people, these features are only of limited use. In this paper, we therefore propose an approach to learning features that are useful for this purpose. To this end, we introduce a self-supervised approach to learning what we call a neural scene decomposition (NSD) that can be exploited for 3D pose estimation. NSD comprises three layers of abstraction to represent human subjects: spatial layout in terms of bounding-boxes and relative depth; a 2D shape representation in terms of an instance segmentation mask; and subject-specific appearance and 3D pose information. By exploiting self-supervision coming from multiview data, our NSD model can be trained end-to-end without any 2D or 3D supervision. In contrast to previous approaches, it works for multiple persons and full-frame images. Because it encodes 3D geometry, NSD can then be effectively leveraged to train a 3D pose estimation network from small amounts of annotated data.