Free-viewpoint human body reenactment aims to generate authentic and coherent poses for
a source subject based on a target body pose skeleton. While current methods are proficient …

Effectively modeling discriminative spatio-temporal information is essential for segmenting
activities in long action sequences. However, we observe that existing methods are limited …

The task of action-driven human motion prediction aims to forecast future human motion
based on the observed sequence while respecting the given action label. It requires …

Diverse human motion prediction (HMP) aims to predict multiple plausible future motions
given an observed human motion sequence. It is a challenging task due to the diversity of …

Ignoring the meaningful kinematics law, which generates improbable or impractical
predictions, is one of the obstacles to human motion prediction. Current methods attempt to …

Predicting accurate and diverse human motion presents a challenging task due to the
complexity and uncertainty of future human motion. Existing works have explored sampling …

Recent generative methods have revolutionized the way of human motion synthesis, such
as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and …

Recent advancements in deep learning and artificial intelligence have underscored the
importance of human motion prediction in fields such as intelligent robotics, autonomous …

Multi-person motion prediction is a complex and emerging field with significant real-world
applications. Current state-of-the-art methods typically adopt dual-path networks to …

Multi-person motion prediction is an emerging and intricate task with broad real-world
applications. Unlike single person motion prediction, it considers not just the skeleton …