MotionCLR

MotionCLR is a motion diffusion model based on an attention mechanism, focusing on the generation and editing of human actions. It uses self-attention and cross-attention mechanisms to simulate intra-modal and inter-modal interactions respectively, achieving fine control and editing of action sequences. The main advantages of this model include editing without training, good interpretability, and the ability to implement a variety of motion editing methods by manipulating attention maps, such as emphasis or weakening of actions, in-place replacement of actions, example-based action generation, etc. The research background of MotionCLR is to solve the shortcomings of previous motion diffusion models in fine-grained editing capabilities and improve the flexibility and accuracy of motion editing through clear text-action correspondences.

MotionCLR's target audience includes animators, game developers, virtual reality content creators, and any professional who needs to generate and edit human motion. The technology is suitable for them because it provides a way to quickly edit and generate motion without training a model, greatly reducing the time and cost of motion capture and animation, while increasing the flexibility and accuracy of motion editing.