17 research outputs found
Long Term Motion Prediction Using Keyposes
Long term human motion prediction is essential in safety-critical
applications such as human-robot interaction and autonomous driving. In this
paper we show that to achieve long term forecasting, predicting human pose at
every time instant is unnecessary. Instead, it is more effective to predict a
few keyposes and approximate intermediate ones by linearly interpolating the
keyposes.
We will demonstrate that our approach enables us to predict realistic motions
for up to 5 seconds in the future, which is far larger than the typical 1
second encountered in the literature. Furthermore, because we model future
keyposes probabilistically, we can generate multiple plausible future motions
by sampling at inference time. Over this extended time period, our predictions
are more realistic, more diverse and better preserve the motion dynamics than
those state-of-the-art methods yield.Comment: Code publicly available at:
https://github.com/senakicir/KeyposePredictio
DE-TGN: Uncertainty-Aware Human Motion Forecasting using Deep Ensembles
Ensuring the safety of human workers in a collaborative environment with
robots is of utmost importance. Although accurate pose prediction models can
help prevent collisions between human workers and robots, they are still
susceptible to critical errors. In this study, we propose a novel approach
called deep ensembles of temporal graph neural networks (DE-TGN) that not only
accurately forecast human motion but also provide a measure of prediction
uncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlo
dropout sampling, we construct a volumetric field representing a range of
potential future human poses based on covariance ellipsoids. To validate our
framework, we conducted experiments using three motion capture datasets
including Human3.6M, and two human-robot interaction scenarios, achieving
state-of-the-art prediction error. Moreover, we discovered that deep ensembles
not only enable us to quantify uncertainty but also improve the accuracy of our
predictions
Diagnostic Spatio-temporal Transformer with Faithful Encoding
This paper addresses the task of anomaly diagnosis when the underlying data
generation process has a complex spatio-temporal (ST) dependency. The key
technical challenge is to extract actionable insights from the dependency
tensor characterizing high-order interactions among temporal and spatial
indices. We formalize the problem as supervised dependency discovery, where the
ST dependency is learned as a side product of multivariate time-series
classification. We show that temporal positional encoding used in existing ST
transformer works has a serious limitation in capturing higher frequencies
(short time scales). We propose a new positional encoding with a theoretical
guarantee, based on discrete Fourier transform. We also propose a new ST
dependency discovery framework, which can provide readily consumable diagnostic
information in both spatial and temporal directions. Finally, we demonstrate
the utility of the proposed model, DFStrans (Diagnostic Fourier-based
Spatio-temporal Transformer), in a real industrial application of building
elevator control