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.  They use L2 loss, where the predicted results are required to be strictly similar to the training samples 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 the prediction task. In this work, we extensively design a novel computationally efficient encoder-decoder model with uncertainty consideration, 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 which 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.

Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

Main Track - Computer Vision

technical paper

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Zhihao Wang

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Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

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