Title: OhMG: Zero-shot Open-vocabulary Human Motion Generation. Abstract: Generating motion in line with text has attracted increasing attention nowadays. However, open-vocabulary human motion generation still remains touchless and undergoes the lack of diverse labeled data. The good news is that, recent studies of large foundation models (e.g., CLIP) have demonstrated superior performance on few/zero-shot image-text alignment, largely reducing the need for manually labeled data. In this paper, we take the advantage of CLIP for open-vocabulary 3D human motion generation in a zero-shot manner. Specifically, our model is composed of two stages, i.e., text2pose and pose2motion generations. For text2pose generation, to address the difficulty of optimization with direct supervision from CLIP, we propose to carve the versatile CLIP model into a slimmer but more specific model for aligning 3D poses and texts, via a novel pipeline distillation strategy. Optimizing with the distilled 3D pose-text model, we manage to concretize the text-pose knowledge of CLIP into a text2pose generator effectively and efficiently. As for pose2motion, drawing the inspiration of the advanced language model, we pretrain a transformer-based motion model, which makes up for the lack of motion dynamics of CLIP. After that, by formulating the condition poses as prompts, the motion generator can generate motions referring to the condition poses in a controllable and flexible manner.