Forecasting future poses can be done accurately by encoding joint trajectories with a discrete cosine transform and feeding the coefficients to a graph network that learns joint dependencies automatically.
Adversarial geometry-aware human mo- tion prediction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning Trajectory Dependencies for Human Motion Prediction
Forecasting future poses can be done accurately by encoding joint trajectories with a discrete cosine transform and feeding the coefficients to a graph network that learns joint dependencies automatically.