Generative Motion

Generative motion research focuses on creating realistic and diverse human and agent movements using computational models. Current efforts concentrate on developing generative models, often employing techniques like diffusion models, transformers, and graph neural networks, to synthesize motion from various inputs such as text, images, music, or even sparse motion data, while addressing challenges like physical plausibility and handling occlusions. These advancements are significant for applications ranging from animation and robotics to autonomous driving and virtual reality, enabling more lifelike simulations and interactions. The field is also actively exploring efficient and robust methods for inverse kinematics, crucial for translating desired movements into feasible robot actions.