Controllable Human Motion Synthesis

Controllable human motion synthesis aims to generate realistic and diverse human movements based on various inputs, such as text descriptions, target trajectories, or musical scores. Current research heavily utilizes diffusion models, often incorporating techniques like latent space manipulation, attention mechanisms, and transformer architectures to achieve fine-grained control over generated motions, including real-time performance. This field is crucial for advancing applications in animation, virtual reality, robotics, and healthcare, particularly by enabling more realistic simulations and interactive experiences. The development of weakly-supervised and efficient methods is a key focus, addressing the limitations of data scarcity and computational cost.