Abstract We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a recurrent …

We focus on the problem of predicting future states of entities in complex, real-world driving
scenarios. Previous research has approached this problem via low-level signals to predict …

Neural sequence models are widely used to model time-series data. Equally ubiquitous is
the usage of beam search (BS) as an approximate inference algorithm to decode output …

We propose UniPose, a unified framework for human pose estimation, based on our"
Waterfall" Atrous Spatial Pooling architecture, that achieves state-of-art-results on several …

We describe a method for articulated human detection and human pose estimation in static
images based on a new representation of deformable part models. Rather than modeling …

Multi-frame human pose estimation has long been a compelling and fundamental problem
in computer vision. This task is challenging due to fast motion and pose occlusion that …

Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

In this work, we introduce the challenging problem of joint multi-person pose estimation and
tracking of an unknown number of persons in unconstrained videos. Existing methods for …

Action recognition and pose estimation from video are closely related tasks for
understanding human motion, most methods, however, learn separate models and combine …

Many practical perception systems exist within larger processes which often include
interactions with users or additional components that are capable of evaluating the quality of …