Plausible Human Motion

Plausible human motion synthesis aims to generate realistic and physically accurate human movements for applications in animation, robotics, and virtual/augmented reality. Current research focuses on developing sophisticated models, including diffusion models, transformers, and variational autoencoders, often incorporating physics-based constraints and scene context to improve realism and address challenges like motion prediction and sparse data handling. These advancements are significantly impacting fields like human-computer interaction and autonomous systems by enabling more natural and believable interactions with digital humans and environments.