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@article{163951,
        author = {Mihir Harne and Parth Gorde and Shreya Junagade and Roma Thakur},
        title = {Texturized Multi-level Implicit Modelling for High- Resolution 3D Human Digitization: The PIFuHD approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2219-2225},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163951},
        abstract = {Our 3D human shape estimation network stands out for integrating volumetric feature transformation, merging diverse image features into 3D space to precisely recover surface geometry. Complemented by a rich dataset of 7000 real-world human models, our method, empowered by unique architecture, excels in single-image 3D human model estimation. Addressing challenges in estimating human pose and body shape from 3D scans over time, we introduce PIFuHD Pixel- aligned Implicit Function. PIFuHD enables end-to-end deep learning for digitizing detailed clothed humans from a single image, surpassing prior work with high-resolution reconstructions on the Render people dataset. Moreover, our innovative approach recovers fine details, even on occluded parts, by transforming shape regression into an aligned image-to-image translation problem. Using a partial texture map as input, our method estimates detailed normal and vector displacement maps, enhancing clothing representation on a low-resolution smooth body model. In the landscape of 3D human shape estimation, our multi-level architecture, balancing broad context and high resolution, significantly outperforms existing techniques, leveraging 1kresolution input images for enhanced single-image reconstructions.},
        keywords = {},
        month = {},
        }