Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

Authors

  • Nadine Rueegg Max Planck Institute for Intelligent Systems
  • Christoph Lassner Amazon
  • Michael Black Max Planck Institute for Intelligent Systems
  • Konrad Schindler ETH Zuerich
  • Nadine Rueegg Max Planck Institute for Intelligent Systems
  • Christoph Lassner Amazon
  • Michael Black Max Planck Institute for Intelligent Systems
  • Konrad Schindler ETH Zuerich

DOI:

https://doi.org/10.1609/aaai.v34i04.6008

Abstract

The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D object parameters. Such an approach works well when supervision is available, but in problems like human pose and shape estimation, it is difficult to obtain natural images with 3D ground truth. To go one step further, we propose a new architecture that facilitates unsupervised, or lightly supervised, learning. The idea is to break the problem into a series of transformations between increasingly abstract representations. Each step involves a cycle designed to be learnable without annotated training data, and the chain of cycles delivers the final solution. Specifically, we use 2D body part segments as an intermediate representation that contains enough information to be lifted to 3D, and at the same time is simple enough to be learned in an unsupervised way. We demonstrate the method by learning 3D human pose and shape from un-paired and un-annotated images. We also explore varying amounts of paired data and show that cycling greatly alleviates the need for paired data. While we present results for modeling humans, our formulation is general and can be applied to other vision problems.

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Published

2020-04-03

How to Cite

Rueegg, N., Lassner, C., Black, M., Schindler, K., Rueegg, N., Lassner, C., Black, M., & Schindler, K. (2020). Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5561-5569. https://doi.org/10.1609/aaai.v34i04.6008

Issue

Section

AAAI Technical Track: Machine Learning