  Predicting future human motion is critical for intelligent robots to interact
with humans in the real world, and human motion has the nature of
multi-granularity. However, most of the existing work either implicitly modeled
multi-granularity information via fixed modes or focused on modeling a single
granularity, making it hard to well capture this nature for accurate
predictions. In contrast, we propose a novel end-to-end network, Semi-Decoupled
Multi-grained Trajectory Learning network (SDMTL), to predict future poses,
which not only flexibly captures rich multi-grained trajectory information but
also aggregates multi-granularity information for predictions. Specifically, we
first introduce a Brain-inspired Semi-decoupled Motion-sensitive Encoding
module (BSME), effectively capturing spatiotemporal features in a
semi-decoupled manner. Then, we capture the temporal dynamics of motion
trajectory at multi-granularity, including fine granularity and coarse
granularity. We learn multi-grained trajectory information using BSMEs
hierarchically and further capture the information of temporal evolutional
directions at each granularity by gathering the outputs of BSMEs at each
granularity and applying temporal convolutions along the motion trajectory.
Next, the captured motion dynamics can be further enhanced by aggregating the
information of multi-granularity with a weighted summation scheme. Finally,
experimental results on two benchmarks, including Human3.6M and CMU-Mocap, show
that our method achieves state-of-the-art performance, demonstrating the
effectiveness of our proposed method. The code will be available if the paper
is accepted.
