The field of 3D vision and human motion modeling is rapidly advancing, with a focus on developing more accurate and efficient methods for tasks such as 3D shape completion, human pose estimation, and motion generation. Recent research has explored the use of generative models, diffusion-based methods, and transformation-based approaches to improve the accuracy and robustness of these tasks. Notably, the use of latent generative paradigms and autoregressive diffusion models has shown promising results in 3D shape completion and motion generation. Additionally, the development of new datasets and evaluation metrics has enabled more comprehensive assessment of model performance. Some noteworthy papers in this area include: Evaluating Latent Generative Paradigms for High-Fidelity 3D Shape Completion, which compares the performance of different generative models for 3D shape completion; Free3D, which proposes a framework for synthesizing realistic 3D motions without 3D motion annotations; and TriDiff-4D, which introduces a novel 4D generative pipeline for producing high-quality, temporally coherent 4D avatars.