Pattern recognition (psychology)

Computer science

Pose

Geography

Machine learning

Jitter

Set (abstract data type)

Computer vision

Telecommunications

Geodesy

Benchmark (surveying)

Deep learning

Artificial intelligence

Code (set theory)

Computer security

Artificial neural network

Monocular

Exploit

Programming language

Frame rate

Frame (networking)

We propose a novel attention-based framework for 3D human pose estimation from a monocular video. 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. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g....

Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction