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Found 3 papers, 3 papers with code
Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions
In this work, we show a systematic design (from 2D to 3D) for how conventional networks and other forms of constraints can be incorporated into the attention framework for learning long-range dependencies for the task of pose estimation.
Ranked #6 on
3D Human Pose Estimation
on HumanEva-I
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction
Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video.
Ranked #15 on
Monocular 3D Human Pose Estimation
on Human3.6M
