Existence Is Chaos: Enhancing 3D Human Motion Prediction with
  Uncertainty Consideration
Zhihao Wang Yulin Zhou Ningyu Zhang Xiaosong Yang Jun Xiao Zhao Wang
Human motion prediction is consisting in forecasting future body poses from
historically observed sequences. It is a longstanding challenge due to motion's
complex dynamics and uncertainty. Existing methods focus on building up
complicated neural networks to model the motion dynamics. The predicted results
are required to be strictly similar to the training samples with L2 loss in
current training pipeline. However, little attention has been paid to the
uncertainty property which is crucial to the prediction task. We argue that the
recorded motion in training data could be an observation of possible future,
rather than a predetermined result. In addition, existing works calculate the
predicted error on each future frame equally during training, while recent work
indicated that different frames could play different roles. In this work, a
novel computationally efficient encoder-decoder model with uncertainty
consideration is proposed, which could learn proper characteristics for future
frames by a dynamic function. Experimental results on benchmark datasets
demonstrate that our uncertainty consideration approach has obvious advantages
both in quantity and quality. Moreover, the proposed method could produce
motion sequences with much better quality that avoids the intractable shaking
artefacts. We believe our work could provide a novel perspective to consider
the uncertainty quality for the general motion prediction task and encourage
the studies in this field. The code will be available in
https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB.