We tackle the problem of generating long-term 3D human motion from multiple action labels.
Two main previous approaches, such as action-and motion-conditioned methods, have …

Real-time in-between motion generation is universally required in games and highly
desirable in existing animation pipelines. Its core challenge lies in the need to satisfy three …

Action recognition has been heavily employed in many applications such as autonomous
vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we …

Music-driven group choreography poses a considerable challenge but holds significant
potential for a wide range of industrial applications. The ability to generate synchronized and …

Skeletal motion plays a vital role in human activity recognition as either an independent data
source or a complement. The robustness of skeleton-based activity recognizers has been …

Deep learning techniques have been employed to design a controllable human motion
synthesizer. Despite their potential, however, designing a neural network-based motion …

Motion prediction is a classic problem in computer vision, which aims at forecasting future
motion given the observed pose sequence. Various deep learning models have been …

Long-term and accurate forecasting is the long-standing pursuit of the human motion
prediction task. Existing methods typically suffer from dramatic degradation in prediction …

The inference of 3D motion and dynamics of the human musculoskeletal system has
traditionally been solved using physics-based methods that exploit physical parameters to …

Although part-based motion synthesis networks have been investigated to reduce the
complexity of modeling heterogeneous human motions, their computational cost remains …