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Found 8 papers, 1 papers with code
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method.
Bonn Activity Maps: Dataset Description
The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest.
Unsupervised and Generic Short-Term Anticipation of Human Body Motions
Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable or even better for very short anticipation times ($<0. 4$ sec) to a recurrent neural network based method.
Human Motion Anticipation with Symbolic Label
We exploit this connection by first anticipating symbolic labels and then generate human motion, conditioned on the human motion input sequence as well as on the forecast labels.
Efficiently and Effectively Recognizing Toricity of Steady State Varieties
We present algorithms and computations on 129 models from the BioModels repository testing for group and coset structures over both the complex numbers and the real numbers.
A Dual-Source Approach for 3D Human Pose Estimation from a Single Image
To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data.
Ranked #37 on
Monocular 3D Human Pose Estimation
on Human3.6M
Efficient Unsupervised Temporal Segmentation of Motion Data
We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence.
A Dual-Source Approach for 3D Pose Estimation from a Single Image
To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval.
Ranked #22 on
3D Human Pose Estimation
on HumanEva-I
