Published Paper Details:

In the realm of 3D human shape estimation, this research introduces an innovative network distinguished by its seamless integration of volumetric feature transformation, a technique designed to merge diverse image features into the 3D space for the precise recovery of surface geometry. Anchored by an extensive dataset comprising 7000 real-world human models, our method, underpinned by a unique architecture, exhibits exceptional proficiency in the challenging domain of single-image 3D human model estimation. Confronting persistent challenges related to the dynamic estimation of human pose and body shape from 3D scans over time, we propose PIFuHD (Pixel-aligned Implicit Function) as a groundbreaking solution. PIFuHD facilitates end-to-end deep learning, enabling the accurate digitization of highly detailed clothed humans from a single image, surpassing previous benchmarks with high-resolution reconstructions on the Render People dataset. A notable feature of our approach lies in its adeptness at recovering fine details, even in occluded regions of the human form. This is achieved by transforming the conventional shape regression process into an aligned image-to-image translation problem. Leveraging a partial texture map as input, our method skillfully estimates detailed normal and vector displacement maps, thereby enhancing clothing representation on a low-resolution smooth body model. Within the expansive landscape of 3D human shape estimation, our multi-level architecture emerges as a beacon of innovation, effectively balancing contextual breadth and high resolution. Significantly, our method outperforms existing techniques by harnessing the potential of 1k-resolution input images, resulting in markedly enhanced single-image reconstructions.