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We present En3D, an enhanced generative scheme for sculpting high-quality 3Dhuman avatars. Unlike previous works that rely on scarce 3D datasets or limited2D collections with imbalanced viewing angles and imprecise pose priors, ourapproach aims to develop a zero-shot 3D generative scheme capable of producingvisually realistic, geometrically accurate and content-wise diverse 3D humanswithout relying on pre-existing 3D or 2D assets. To address this challenge, weintroduce a meticulously crafted workflow that implements accurate physicalmodeling to learn the enhanced 3D generative model from synthetic 2D data.During inference, we integrate optimization modules to bridge the gap betweenrealistic appearances and coarse 3D shapes. Specifically, En3D comprises threemodules: a 3D generator that accurately models generalizable 3D humans withrealistic appearance from synthesized balanced, diverse, and structured humanimages; a geometry sculptor that enhances shape quality using multi-view normalconstraints for intricate human anatomy; and a texturing module thatdisentangles explicit texture maps with fidelity and editability, leveragingsemantical UV partitioning and a differentiable rasterizer. Experimentalresults show that our approach significantly outperforms prior works in termsof image quality, geometry accuracy and content diversity. We also showcase theapplicability of our generated avatars for animation and editing, as well asthe scalability of our approach for content-style free adaptation.

En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data