Stochastic human motion prediction aims to forecast multiple plausible future
motions given a single pose sequence from the past. Most previous works focus
on designing elaborate losses to improve the accuracy, while the diversity is
typically characterized by randomly sampling a set of latent variables from the
latent prior, which is then decoded into possible motions. This joint training
of sampling and decoding, however, suffers from posterior collapse as the
learned latent variables tend to be ignored by a strong decoder, leading to
limited diversity. Alternatively, inspired by the diffusion process in
nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic
model to treat the kinematics of human joints as heated particles, which will
diffuse from original states to a noise distribution. This process offers a
natural way to obtain the "whitened" latents without any trainable parameters,
and human motion prediction can be regarded as the reverse diffusion process
that converts the noise distribution into realistic future motions conditioned
on the observed sequence. Specifically, MotionDiff consists of two parts: a
spatial-temporal transformer-based diffusion network to generate diverse yet
plausible motions, and a graph convolutional network to further refine the
outputs. Experimental results on two datasets demonstrate that our model yields
the competitive performance in terms of both accuracy and diversity.

该研究提出了一种名为 MotionDiff 的扩散概率模型，将人体运动的运动学视为受热的粒子，其自原始状态扩散到噪声分布。该方法通过一种自然的方式获取了 “白化” 的潜在变量，而无需任何可训练参数，证明 MotionDiff 模型在两种数据集上具有竞争力的准确性和多样性表现。