Abstract: The reconstruction of 3D human models from monocular RGB video faces challenges in accurately capturing human poses,especially when using prior models like SMPL.Due to its rigid assumptions,such models struggle to depict subtle pose variations,leading to suboptimal reconstruction results.Additionally,existing NeRF-based human modeling methods often generate unnatural shadows or floating artifacts around certain body parts when rendering unseen poses,and their representation of texture details tends to be insufficient.To address these issues,this paper proposes a hierarchical network based on the Triplane Multiscale learning,aims at enhancing the texture details of 3D human models through NeRF techniques and improving the model's generalization capability across different poses.In terms of methodology,multi-resolution hash encoding is employed to replace the traditional sinusoidal frequency encoding function,allowing for more efficient capture of high-frequency human features and speeding up model convergence.The Triplane Multiscale learning strategy is applied to capture pose details,effectively improving the accuracy and visual quality of 3D reconstructions.Experiments demonstrate that the proposed improvements significantly enhance the reconstruction of 3D human models,especially when handling complex pose variations.The method shows notable advantages in terms of training speed,rendering quality,and pose generalization capabilities.By applying this model,the resulting 3D human models exhibit more realistic details,and the synthesized results for novel poses are of high quality,further advancing the development of 3D human reconstruction technology from monocular video.

Key words: Neural radiance field, SMPL, Human reconstruction, Deep learning, MLP