SynHMR: Synergistic Joint-Mesh Modeling for LiDAR-based Human Mesh Reconstruction

LiDAR-based Human Mesh Reconstruction (HMR) is crucialfor understanding human activities in real-world environments. How-ever, the inherent sparsity of LiDAR point clouds often causes severeloss of anatomical details, limiting reconstruction accuracy. Althoughrecent methods have made promising progress, most still follow a decou-pled pipeline that first estimates skeletal joints and then reconstructsthe mesh, leading to error propagation under sparse observations. Toaddress this issue and fully exploit both the topological guidance of theskeleton on the mesh and the spatial constraints of the surface mesh onthe skeleton, we propose SynHMR, a Synergistic Joint-Mesh Modelingframework for robust LiDAR-based HMR. Specifically, we first initializeboth the skeletal joints and the mesh vertices as queries equipped withpositional information for co-optimization. We then design a Geometry-Aware Transformer that leverages point cloud features to apply the at-tention mechanism between joint and vertex queries, enabling their it-erative refinement through mutual information exchange. Furthermore,we introduce a Noise-Augmented Learning strategy that injects per-turbed joint queries during training. This reduces the impact of noisewhile encouraging bidirectional association between joints and mesh. Ex-tensive experiments on LiDARHuman26M, SLOPER4D, and Human-M3 demonstrate that SynHMR achieves state-of-the-art performanceamong LiDAR-based HMR approaches. The project page is availableat https://github.com/ShiRui1208/SynHMR.