MotionCLR is an attention mechanism-based motion diffusion model focused on generating and editing human actions. It achieves fine control and editing of motion sequences through self-attention and cross-attention mechanisms, simulating interactions both within and between modalities. The main advantages of this model include the ability to edit without training, good interpretability, and the capability to implement various motion editing methods by manipulating the attention maps, such as emphasizing or diminishing actions, in-place action replacement, and example-based action generation. The research background of MotionCLR is to address the shortcomings of previous motion diffusion models in fine-grained editing capabilities, enhancing the flexibility and precision of motion editing through clear text-action correspondence.