79 papers with code • 0 benchmarks • 12 datasets
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Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem.
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method.
Some of these approaches have also shown that these attacks are feasible in the real-world, i. e. by modifying an object and filming it with a video camera.
Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI.
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification.
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.