  The increased demand for 3D data in AR/VR, robotics and gaming applications,
gave rise to powerful generative pipelines capable of synthesizing high-quality
3D objects. Most of these models rely on the Score Distillation Sampling (SDS)
algorithm to optimize a 3D representation such that the rendered image
maintains a high likelihood as evaluated by a pre-trained diffusion model.
However, finding a correct mode in the high-dimensional distribution produced
by the diffusion model is challenging and often leads to issues such as
over-saturation, over-smoothing, and Janus-like artifacts. In this paper, we
propose a novel learning paradigm for 3D synthesis that utilizes pre-trained
diffusion models. Instead of focusing on mode-seeking, our method directly
models the distribution discrepancy between multi-view renderings and diffusion
priors in an adversarial manner, which unlocks the generation of high-fidelity
and photorealistic 3D content, conditioned on a single image and prompt.
Moreover, by harnessing the latent space of GANs and expressive diffusion model
priors, our method facilitates a wide variety of 3D applications including
single-view reconstruction, high diversity generation and continuous 3D
interpolation in the open domain. The experiments demonstrate the superiority
of our pipeline compared to previous works in terms of generation quality and
diversity.
