Existing Human Mesh Recovery (HMR) methods typically assume a standard human body structure, overlooking diverse anatomical conditions such as limb loss or mobility impairments. This assumption biases the models when applied to individuals with disabilities—a shortcoming further exacerbated by the limited availability of suitable datasets. To address this gap, we propose Amputated Joint Aware Human Recovery (AJAHR), which is an adaptive pose estimation framework that enhances mesh reconstruction for individuals with impairments. Our model incorporates a body-part amputation classifier—jointly trained alongside human mesh recovery—to detect potential amputations. We also introduce Amputee 3D (A3D), a synthetic dataset offering a wide range of amputee poses for more robust training. While maintaining strong performance on non-amputees, our approach achieves state-of-the-art results for amputated individuals.