Back to MLP: A Simple Baseline for Human Motion Prediction
- URL: http://arxiv.org/abs/2207.01567v1
- Date: Mon, 4 Jul 2022 16:35:58 GMT
- Title: Back to MLP: A Simple Baseline for Human Motion Prediction
- Authors: Wen Guo, Yuming Du, Xi Shen, Vincent Lepetit, Xavier Alameda-Pineda,
Francesc Moreno-Noguer
- Abstract summary: This paper tackles the problem of human motion prediction, consisting in forecasting future body poses from historically observed sequences.
We show that the performance of these approaches can be surpassed by a light-weight and purely architectural architecture with only 0.14M parameters.
An exhaustive evaluation on Human3.6M, AMASS and 3DPW datasets shows that our method, which we dub siMLPe, consistently outperforms all other approaches.
- Score: 59.18776744541904
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper tackles the problem of human motion prediction, consisting in
forecasting future body poses from historically observed sequences. Despite of
their performance, current state-of-the-art approaches rely on deep learning
architectures of arbitrary complexity, such as Recurrent Neural Networks~(RNN),
Transformers or Graph Convolutional Networks~(GCN), typically requiring
multiple training stages and more than 3 million of parameters. In this paper
we show that the performance of these approaches can be surpassed by a
light-weight and purely MLP architecture with only 0.14M parameters when
appropriately combined with several standard practices such as representing the
body pose with Discrete Cosine Transform (DCT), predicting residual
displacement of joints and optimizing velocity as an auxiliary loss.
An exhaustive evaluation on Human3.6M, AMASS and 3DPW datasets shows that our
method, which we dub siMLPe, consistently outperforms all other approaches. We
hope that our simple method could serve a strong baseline to the community and
allow re-thinking the problem of human motion prediction and whether current
benchmarks do really need intricate architectural designs. Our code is
available at \url{https://github.com/dulucas/siMLPe}.
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