A key step towards understanding human behavior is the prediction of 3D human
motion. Successful solutions have many applications in human tracking, HCI, and
graphics. Most previous work focuses on predicting a time series of future 3D
joint locations given a sequence 3D joints from the past. This Euclidean
formulation generally works better than predicting pose in terms of joint
rotations. Body joint locations, however, do not fully constrain 3D human pose,
leaving degrees of freedom undefined, making it hard to animate a realistic
human from only the joints. Note that the 3D joints can be viewed as a sparse
point cloud. Thus the problem of human motion prediction can be seen as point
cloud prediction. With this observation, we instead predict a sparse set of
locations on the body surface that correspond to motion capture markers. Given
such markers, we fit a parametric body model to recover the 3D shape and pose
of the person. These sparse surface markers also carry detailed information
about human movement that is not present in the joints, increasing the
naturalness of the predicted motions. Using the AMASS dataset, we train MOJO,
which is a novel variational autoencoder that generates motions from latent
frequencies. MOJO preserves the full temporal resolution of the input motion,
and sampling from the latent frequencies explicitly introduces high-frequency
components into the generated motion. We note that motion prediction methods
accumulate errors over time, resulting in joints or markers that diverge from
true human bodies. To address this, we fit SMPL-X to the predictions at each
time step, projecting the solution back onto the space of valid bodies. These
valid markers are then propagated in time. Experiments show that our method
produces state-of-the-art results and realistic 3D body animations. The code
for research purposes is at this https URL

本文提出了一种基于稀疏表面标记预测人体运动的方法，通过 MOJO 模型生成高频成分的动画，使用 SMPL-X 模型保证解决方案符合身体的真实形态，并取得了最先进的结果。