Human Mesh Recovery (HMR) frameworks predict a comprehensive 3D mesh of an observed human based on sensor measurements. The majority of these frameworks are purely image-based. Despite the richness of this data, image-based HMR frameworks are vulnerable to depth ambiguity, resulting in mesh inaccuracies in 3D space. Several HMR frameworks in the literature use LiDAR data to avoid this depth ambiguity. However, these frameworks are either only LiDAR-based, which limits performance due to LiDAR sparseness and limited training data, or they are model-free, making the resulting meshes vulnerable to artifacts and limited detail. Therefore, this work introduces SMPLify-3D, an optimization-based HMR framework that combines the richness of image data and the depth information within sparse LiDAR data to improve the 3D mesh inaccuracies of image-based HMR frameworks. The proposed framework consists of three main steps: 1) a body part visibility filter based on the 2D detected keypoints, 2) rough alignment between the mesh and the observed LiDAR point cloud using the ICP algorithm, and 3) an optimization scheme, inspired by SMPLify, that modifies the actual pose and shape of the mesh to improve both the 3D and image alignment. SMPLify-3D is versatile compared to other methods and outperforms image-based and SMPL-compatible LiDAR-based HMR frameworks by improving the Per-Vertex-Error (PVE) with 45% and 26% on the 3DPW and HumanM3 datasets respectively. Multiple quantitative experiments are conducted to show the effects of LiDAR noise and sparsity on the framework’s performance. Additionally, qualitative results illustrate how the proposed method achieves superior results on out-of-sample data recorded by a mobile robot. The source code for this work is available at: https://github.com/guidodumont/SMPLify-3D