Point cloud data has become a fundamental modality for human body modeling and pose estimation because of their explicit and direct representation of 3D geometric structure. In contrast to image-based or volumetric methods，point clouds preserve raw geometric information without discretization or projective distortion，making them especially suitable for capturing detailed surface characteristics. However，in practical acquisition scenarios，such as those using consumer-grade LiDAR sensors or depth cameras，point clouds often suffer from substantial local missing regions caused by sensor viewpoint limitations，object self-occlusions，and environmental obstructions. These incompleteness issues severely degrade the completeness and accuracy of reconstruction，posing substantial challenges for downstream tasks such as animation，virtual try-on，or motion analysis. Moreover，as unstructured data with irregular sampling，point clouds lack explicit connectivity information and exhibit permutation invariance. This inherent nature complicates the learning of continuous surface geometry and topological connectivity of the human body，which is essential for recovering plausible shapes and joint kinematics. Consequently，many existing approaches produce results with structural distortions，loss of fine-grained details，or inaccurate pose predictions，especially around articulated regions such as elbows，knees，and shoulders. Although recent deep learning models have made progress in processing point sets，they still struggle to maintain structural coherence and geometric fidelity when handling highly incomplete inputs under complex articulations. Therefore，methods that can infer missing geometry with strong contextual awareness and robust priors on human body shape and mobility are still needed.

To address these issues，we propose a novel method for human body reconstruction and point cloud completion by leveraging the topological prior of the skinned multiperson linear （SMPL） model. Our approach begins with a multiscale feature extraction module that incorporates per-point semantic awareness，enabling the network to capture fine-grained details and broader contextual information. This module employs a combination of local geometric feature learners and hierarchical aggregation layers to enhance the understanding of structural patterns at various scales. By integrating point-wise semantics during the feature learning process，it effectively encodes biological constraints into the representation，thereby generating more discriminative and context-aware point cloud features that better preserve the natural articulation and proportionality of human body shapes. Based on this enriched feature representation，the parametric mesh topology of the SMPL model guides the prediction of an initial deformable human skeleton. This step effectively regularizes the structural reasoning in missing or incomplete regions by introducing strong anatomical constraints，considerably alleviating topological ambiguities caused by severe data incompleteness，such as occlusions or large-scale scanning artifacts. The parametric model acts as a structural template that ensures biomechanically consistent predictions，even when large portions of the input are absent. As a result，the proposed framework maintains robust performance under challenging conditions，producing plausible human body proportions and pose-dependent shape variations that align with realistic anatomical properties. Furthermore，we design a vertex offset-aware refinement module that learns spatial residuals between the input sparse point cloud and the vertices of the predicted mesh through fine-grained alignment. This module operates in a hierarchical manner，progressively correcting geometric discrepancies across multiple resolution levels. It utilizes a cascaded residual learner that first handles large-scale deformations and then gradually focuses on recovering subtle surface details，such as muscle curves，soft-tissue contours，and joint depressions，which are often lost in coarse reconstruction stages. By iteratively aligning the predicted mesh with the observed points，our method achieves precise reconstruction of detailed surface geometry with remarkably improved geometric fidelity. The final output is a high-fidelity and anatomically plausible human body model，even when reconstructed from highly incomplete or noisy input data.

Experimental results demonstrate that the proposed method consistently outperforms existing approaches across the SURREAL and AMASS datasets，highlighting its effectiveness and broad applicability. On the SURREAL dataset，the method reduces the vertex-to-vertex reconstruction error by 4.6%，setting a new state-of-the-art with a remarkable pose estimation accuracy of 16.2 mm. This improvement not only reflects enhanced precision in capturing complex human motions but also indicates stronger alignment with ground-truth data. Similarly，on the AMASS dataset，the approach achieves a considerably lower Chamfer distance compared with current point cloud completion techniques，underscoring its ability to generate more geometrically consistent and structurally accurate reconstructions. These outcomes across diverse datasets further validate the strong generalization capacity and robustness of the proposed framework，suggesting its potential for real-world applications where reliability under varying conditions is critical.

In conclusion，this work demonstrates that the effective integration of SMPL’s topological prior into the point cloud reconstruction pipeline leads to remarkable improvements in reconstruction quality. This approach proves especially advantageous when dealing with unstructured and highly incomplete input data，where traditional methods often struggle to produce plausible results. By leveraging the strong semantic and structural constraints provided by the SMPL model，our method enhances detail recovery，ensures improved shape consistency，and reduces artifacts in challenging scenarios. These findings highlight the value of incorporating semantic priors in geometric reconstruction tasks and suggest promising directions for future research in robust 3D understanding and generation.