Optimising 2D Pose Representations: Improving Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation-Reference-Cited by-同舟云学术

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Optimising 2D Pose Representations: Improving Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation

Published:2023-11-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 20th ACM SIGGRAPH European Conference on Visual Media Production
language:
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Author:

Hardy Peter Timothy David¹^ORCID,Dasmahapatra Srinandan¹^ORCID,Kim Hansung¹^ORCID

Affiliation:

1. University of Southampton, UK

Funder

Engineering & Physical Sciences Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626495.3626505

Reference35 articles.

1. Martín Arjovsky and Léon Bottou . 2017. Towards Principled Methods for Training Generative Adversarial Networks. ArXiv abs/1701.04862 ( 2017 ). Martín Arjovsky and Léon Bottou. 2017. Towards Principled Methods for Training Generative Adversarial Networks. ArXiv abs/1701.04862 (2017).

2. Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks

3. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

4. 3D Human Pose Estimation = 2D Pose Estimation + Matching

5. Unsupervised 3D Pose Estimation With Geometric Self-Supervision

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