The field of human motion and interaction modeling is rapidly advancing, with a focus on developing more realistic and nuanced models of human behavior. Recent research has emphasized the importance of capturing complex interactions between humans and objects, as well as modeling subtle movements and emotions. One notable trend is the use of large language models and diffusion-based approaches to generate more realistic and controllable human motions. Additionally, there is a growing interest in developing methods for estimating articulated object models and generating realistic hand-object interactions. These advances have significant implications for a range of applications, including computer graphics, robotics, and virtual reality. Noteworthy papers in this area include InterPose, which introduces a large-scale dataset for human-object interaction, and SMooGPT, which proposes a novel approach for stylized motion generation using large language models. These papers demonstrate the potential for significant advancements in human motion and interaction modeling, and highlight the importance of continued research in this area.