A Novel Joint Points and Silhouette-Based Method to Estimate 3D Human Pose and Shape

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Abstract

This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes in 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit a parametric model to the silhouettes. Our approach uses comprehensive shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Datavetenskap (datalogi)
  • Datorseende och robotik (autonoma system)
Originalspråkengelska
Titel på värdpublikationPattern Recognition. ICPR International Workshops and Challenges. ICPR 2021
FörlagSpringer
Sidor41-56
ISBN (tryckt)978-3-030-68762-5
StatusPublished - 2021
PublikationskategoriForskning
Peer review utfördJa

Publikationsserier

Namn Lecture Notes in Computer Science
FörlagSpringer
Volym12661
ISSN (elektroniskt)1611-3349