Generating Smooth Pose Sequences for Diverse Human Motion Prediction
- URL: http://arxiv.org/abs/2108.08422v2
- Date: Sat, 21 Aug 2021 14:20:00 GMT
- Title: Generating Smooth Pose Sequences for Diverse Human Motion Prediction
- Authors: Wei Mao, Miaomiao Liu, Mathieu Salzmann
- Abstract summary: We introduce a unified deep generative network for both diverse and controllable motion prediction.
Our experiments on two standard benchmark datasets, Human3.6M and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art baselines in terms of both sample diversity and accuracy.
- Score: 90.45823619796674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent progress in stochastic motion prediction, i.e., predicting multiple
possible future human motions given a single past pose sequence, has led to
producing truly diverse future motions and even providing control over the
motion of some body parts. However, to achieve this, the state-of-the-art
method requires learning several mappings for diversity and a dedicated model
for controllable motion prediction. In this paper, we introduce a unified deep
generative network for both diverse and controllable motion prediction. To this
end, we leverage the intuition that realistic human motions consist of smooth
sequences of valid poses, and that, given limited data, learning a pose prior
is much more tractable than a motion one. We therefore design a generator that
predicts the motion of different body parts sequentially, and introduce a
normalizing flow based pose prior, together with a joint angle loss, to achieve
motion realism.Our experiments on two standard benchmark datasets, Human3.6M
and HumanEva-I, demonstrate that our approach outperforms the state-of-the-art
baselines in terms of both sample diversity and accuracy. The code is available
at https://github.com/wei-mao-2019/gsps
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