  Generating multi-view images from a single input view using image-conditioned
diffusion models is a recent advancement and has shown considerable potential.
However, issues such as the lack of consistency in synthesized views and
over-smoothing in extracted geometry persist. Previous methods integrate
multi-view consistency modules or impose additional supervisory to enhance view
consistency while compromising on the flexibility of camera positioning and
limiting the versatility of view synthesis. In this study, we consider the
radiance field optimized during geometry extraction as a more rigid consistency
prior, compared to volume and ray aggregation used in previous works. We
further identify and rectify a critical bias in the traditional radiance field
optimization process through score distillation from a multi-view diffuser. We
introduce an Unbiased Score Distillation (USD) that utilizes unconditioned
noises from a 2D diffusion model, greatly refining the radiance field fidelity.
We leverage the rendered views from the optimized radiance field as the basis
and develop a two-step specialization process of a 2D diffusion model, which is
adept at conducting object-specific denoising and generating high-quality
multi-view images. Finally, we recover faithful geometry and texture directly
from the refined multi-view images. Empirical evaluations demonstrate that our
optimized geometry and view distillation technique generates comparable results
to the state-of-the-art models trained on extensive datasets, all while
maintaining freedom in camera positioning. Please see our project page at
https://youjiazhang.github.io/USD/.
