The recent advancements in human motion and mesh recovery have seen a significant shift towards probabilistic and generative modeling approaches, which aim to address the inherent ambiguities and uncertainties in 2D-to-3D mapping tasks. These methods leverage deep learning techniques to model and sample from probabilistic distributions, thereby enhancing the robustness and precision of 3D reconstruction. Key innovations include the integration of uncertainty quantification into the learning process, the use of conditional masked transformers for pose token learning, and the incorporation of environmental constraints to refine motion estimates. These developments not only improve the accuracy of human motion and mesh recovery but also pave the way for more realistic and context-aware applications in augmented and virtual reality. Notably, methods like EnvPoser and CUPS have demonstrated state-of-the-art performance by combining uncertainty modeling with environmental context and conformal prediction, respectively. Dyn-HaMR and CondiMen also stand out for their advancements in 4D hand motion reconstruction from dynamic cameras and multi-person mesh recovery with Bayesian networks, respectively. These contributions collectively push the boundaries of what is possible in human motion and mesh recovery, offering new benchmarks and practical applications.