Human mesh recovery (HMR) is crucial in many computer vision applications; from health to entertainment, among others. HMR from monocular images has predominantly been addressed by deterministic methods that output a single prediction for a given $2D$ image. However, HMR from a single image is an ill-posed problem due to depth ambiguity and occlusions. Probabilistic methods have attempted to address this by generating and fusing multiple plausible $3D$ reconstructions, but their performance has often lagged behind deterministic approaches. In this paper, we introduce $\textbf{GenHMR}$, a novel generative framework that reformulates monocular HMR as an image-conditioned generative task, explicitly modeling and mitigating uncertainties in the $2D \rightarrow 3D$ mapping process. GenHMR comprises two key components: (1) $\textbf{a pose tokenizer}$ to convert $3D$ human poses into a sequence of discrete tokens in a latent space, and (2) $\textbf{an image-conditional masked transformer}$ to learn the probabilistic distributions of the pose tokens, conditioned on the input image prompt along with the randomly masked token sequence. During $\textit{inference}$, the model samples from the learned conditional distribution to iteratively decode high-confidence pose tokens, thereby reducing $3D$ reconstruction uncertainties. To further refine the reconstruction, a $2D$ pose-guided refinement technique is proposed to directly fine-tune the decoded pose tokens in the latent space, which forces the projected $3D$ body mesh to align with the $2D$ pose clues. Experiments on benchmark datasets demonstrate that GenHMR significantly outperforms state-of-the-art methods. The project website can be found at \url{https://anonymous-ai-model.github.io/GenHMR/}