Summary of Epoch: Jointly Estimating the 3d Pose Of Cameras and Humans, by Nicola Garau et al.
EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans
by Nicola Garau, Giulia Martinelli, Niccolò Bisagno, Denis Tomè, Carsten Stoll
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors of this paper propose a novel approach to monocular human pose estimation (HPE), which involves utilizing the full perspective camera model rather than relying on approximations. The EPOCH framework consists of two main components: LiftNet and RegNet. LiftNet estimates the 3D pose from 2D pose and camera parameters in an unsupervised manner, while RegNet predicts a 3D pose using only 2D pose data as weak supervision. The authors achieve state-of-the-art results on the Human3.6M and MPI-INF-3DHP datasets and demonstrate better generalization to unseen data when modeling the lifting as an unsupervised task with a camera in-the-loop. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using cameras to estimate where people’s joints are in 3D space from just one 2D picture. It wants to get away from using simple math shortcuts and instead use the real rules of how cameras work. The team created something called EPOCH, which has two parts: LiftNet and RegNet. LiftNet takes a 2D picture and camera information and makes an estimate of where joints are in 3D space without needing any extra help. RegNet starts with just one 2D picture and figures out the 2D pose and camera information. It then uses that to make its own guess about joint positions in 3D space. |
Keywords
» Artificial intelligence » Generalization » Pose estimation » Unsupervised
