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Monocular 3D clothed human reconstruction aims to create a complete 3D avatarfrom a single image. To tackle the human geometry lacking in one RGB image,current methods typically resort to a preceding model for an explicit geometricrepresentation. For the reconstruction itself, focus is on modeling both it andthe input image. This routine is constrained by the preceding model, andoverlooks the integrity of the reconstruction task. To address this, this paperintroduces a novel paradigm that treats human reconstruction as a holisticprocess, utilizing an end-to-end network for direct prediction from 2D image to3D avatar, eliminating any explicit intermediate geometry display. Based onthis, we further propose a novel reconstruction framework consisting of twocore components: the Anatomy Shaping Extraction module, which captures implicitshape features taking into account the specialty of human anatomy, and theTwins Negotiating Reconstruction U-Net, which enhances reconstruction throughfeature interaction between two U-Nets of different modalities. Moreover, wepropose a Comic Data Augmentation strategy and construct 15k+ 3D human scans tobolster model performance in more complex case input. Extensive experiments ontwo test sets and many in-the-wild cases show the superiority of our methodover SOTA methods. Our demos can be found in :https://e2e3dgsrecon.github.io/e2e3dgsrecon/.

Unify3D: An Augmented Holistic End-to-end Monocular 3D Human Reconstruction via Anatomy Shaping and Twins Negotiating