Reconstructing clothed human bodies from a single perspective-distorted image is a fundamental yet challenging problem in computer vision，with broad applications in virtual try-on，digital human creation，AR/VR content generation，and human-computer interaction. Although recent advances in monocular human reconstruction have achieved impressive progress，most existing approaches implicitly assume orthographic or weak-perspective projection models. These simplified assumptions neglect the actual imaging geometry of real cameras，reducing depth to a single global scaling factor and failing to capture the inherently nonlinear spatial variations caused by strong perspective distortion. Consequently，the reconstructed geometry often suffers from inaccurate limb proportions，inconsistent body scale，and distortion-dependent artifacts，particularly when the subject is close to the camera or occupies a non-central region of the image. Such limitations significantly undermine the applicability of current methods to real-world scenarios where perspective effects are inevitable.To address these shortcomings，This study proposes PerRec，a novel perspective-aware reconstruction framework specifically designed for recovering high-fidelity clothed human geometry from a single perspective-distorted RGB image.

The key idea of PerRec is to explicitly account for perspective-induced depth ambiguity and projection nonlinearity through a combination of distortion decoupling，geometric field representation，and pseudo-multiple-view supervision. Unlike existing models that attempt to implicitly learn perspective from data，PerRec introduces structural priors and tailored network designs that directly target the characteristics of perspective distortion，thereby enabling a more accurate and physically plausible 3D inference.The proposed framework contains four major components. First，a distortion decoupling module is introduced to separate image position from the underlying perspective distortion pattern. The method effectively normalizes spatial variance while preserving layout-dependent cues by uniformly partitioning the input image and generating virtual viewpoint transformations for each region. This mechanism allows the model to effectively capture how projection distortion changes across the field of view，mitigating the tendency of conventional models to collapse depth variations into oversimplified estimates. Second，we design a multi-scale attention hourglass network （MA-HGNet） to robustly extract local and global features under varying degrees of distortion. The network integrates a multi-level hourglass structure with channel attention mechanisms to refine fine-grained patterns，such as clothing folds，body boundaries，and high-curvature regions that are particularly susceptible to distortion. MA-HGNet demonstrates enhanced sensitivity to spatially localized distortions while maintaining stable global shape perception compared with standard convolutional backbones. Third，we convert the SMPL mesh into a Fourier occupancy field （FOF） to effectively capture continuous geometry and enhance high-frequency surface details. This representation encodes occupancy values using Fourier basis functions，enabling the network to model sharp transitions，intricate clothing topology，and subtle geometric variations that are difficult to represent with parametric models alone. The proposed model unifies the advantages of parametric priors and implicit fields，resulting in substantially detailed and expressive reconstructions. Finally，considering that a single image inherently lacks sufficient viewpoint diversity，we introduce a pseudo-multi-view module that synthesizes multiple virtual observations during training. These synthesized views act as implicit geometric constraints，guiding the network to learn distortion-invariant features and improving its ability to reason about occluded or heavily distorted regions. This strategy significantly boosts reconstruction robustness without requiring additional input views or multi-camera setups.

Extensive experiments are conducted on the Thuman2.0 and CustomHumans datasets，covering controlled and in-the-wild settings with diverse degrees of perspective distortion. Quantitative evaluations demonstrate that PerRec substantially outperforms state-of-the-art monocular reconstruction methods across multiple metrics，including chamfer distance，normal consistency，and point-to-surface error. Specifically，improvements of approximately 60%–80% are observed on average，highlighting the effectiveness of the proposed design in addressing global and local reconstruction quality. Qualitative comparisons further show that PerRec produces a stable body scale，highly realistic limb proportions，and detailed clothing geometry，particularly in strongly distorted images where competing methods typically fail.

The contributions of this study are threefold. First，this study provides a comprehensive analysis of the limitations of existing projection assumptions in monocular reconstruction and establishes perspective distortion as a core challenge in real-world 3D human modeling. Second，this study proposes a unified reconstruction framework that integrates distortion decoupling，implicit field representation，and pseudo-multi-view learning，offering a principled and effective solution for handling perspective effects. Third，this study demonstrates that accurate and high-fidelity clothed human reconstruction from a single perspective-distorted image is achievable without relying on multi-view capture systems or camera calibration. Overall，this study offers a practical and extensible perspective-aware solution for real-world human reconstruction，bridging the gap between theoretical projection modeling and data-driven 3D inference. The proposed approach not only enhances geometric accuracy under challenging perspective conditions but also expands the applicability of monocular reconstruction methods to everyday images captured by mobile devices and consumer cameras. Results indicate that perspective distortion，long regarded as a major obstacle for monocular reconstruction，can be effectively resolved by combining structural modeling cues with carefully designed learning-based strategies.