# Action Generation

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.

Mochi 1 is an open-source video generation model introduced by Genmo as a research preview version, aiming to address fundamental issues in the current AI video landscape. The model is renowned for its unparalleled motion quality, exceptional prompt-following capabilities, and its ability to bridge the uncanny valley, generating coherent and fluid human actions and expressions. Mochi 1 was developed in response to the growing demand for high-quality video content, particularly in the gaming, film, and entertainment industries. A free trial is currently available, though detailed pricing information is not provided on the page.