MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware
  Diffusion and Iterative Refinement
Authors: name: Xu He, name: Xiaoyu Li, name: Di Kang, name: Jiangnan Ye, name: Chaopeng Zhang, name: Liyang Chen, name: Xiangjun Gao, name: Han Zhang, name: Zhiyong Wu, name: Haolin Zhuang

Existing works in single-image human reconstruction suffer from weak
generalizability due to insufficient training data or 3D inconsistencies for a
lack of comprehensive multi-view knowledge. In this paper, we introduce
MagicMan, a human-specific multi-view diffusion model designed to generate
high-quality novel view images from a single reference image. As its core, we
leverage a pre-trained 2D diffusion model as the generative prior for
generalizability, with the parametric SMPL-X model as the 3D body prior to
promote 3D awareness. To tackle the critical challenge of maintaining
consistency while achieving dense multi-view generation for improved 3D human
reconstruction, we first introduce hybrid multi-view attention to facilitate
both efficient and thorough information interchange across different views.
Additionally, we present a geometry-aware dual branch to perform concurrent
generation in both RGB and normal domains, further enhancing consistency via
geometry cues. Last but not least, to address ill-shaped issues arising from
inaccurate SMPL-X estimation that conflicts with the reference image, we
propose a novel iterative refinement strategy, which progressively optimizes
SMPL-X accuracy while enhancing the quality and consistency of the generated
multi-views. Extensive experimental results demonstrate that our method
significantly outperforms existing approaches in both novel view synthesis and
subsequent 3D human reconstruction tasks.