Human Motion Synthesis

Human motion synthesis aims to generate realistic and controllable human movements using computational methods, primarily focusing on creating animations for various applications. Current research heavily utilizes deep learning, particularly diffusion models and transformers, often incorporating multimodal conditioning (text, audio, object motion) to achieve greater realism and fine-grained control over the synthesized motions. This field is significant for its potential impact on animation, virtual reality, robotics, and healthcare, enabling more lifelike virtual characters, improved human-computer interaction, and the creation of synthetic datasets to address data scarcity issues in related research areas.