Single-image human reconstruction is vital for digital human modeling
applications but remains an extremely challenging task. Current approaches rely
on generative models to synthesize multi-view images for subsequent 3D
reconstruction and animation. However, directly generating multiple views from
a single human image suffers from geometric inconsistencies, resulting in
issues like fragmented or blurred limbs in the reconstructed models. To tackle
these limitations, we introduce HumanDreamer-X, a novel framework that
integrates multi-view human generation and reconstruction into a unified
pipeline, which significantly enhances the geometric consistency and visual
fidelity of the reconstructed 3D models. In this framework, 3D Gaussian
Splatting serves as an explicit 3D representation to provide initial geometry
and appearance priority. Building upon this foundation, HumanFixer is
trained to restore 3DGS renderings, which guarantee photorealistic results.
Furthermore, we delve into the inherent challenges associated with attention
mechanisms in multi-view human generation, and propose an attention modulation
strategy that effectively enhances geometric details identity consistency
across multi-view. Experimental results demonstrate that our approach markedly
improves generation and reconstruction PSNR quality metrics by 16.45% and
12.65%, respectively, achieving a PSNR of up to 25.62 dB, while also showing
generalization capabilities on in-the-wild data and applicability to various
human reconstruction backbone models.