  Recent advances in diffusion models have enabled 3D generation from a single
image. However, current methods often produce suboptimal results for novel
views, with blurred textures and deviations from the reference image, limiting
their practical applications. In this paper, we introduce HiFi-123, a method
designed for high-fidelity and multi-view consistent 3D generation. Our
contributions are twofold: First, we propose a Reference-Guided Novel View
Enhancement (RGNV) technique that significantly improves the fidelity of
diffusion-based zero-shot novel view synthesis methods. Second, capitalizing on
the RGNV, we present a novel Reference-Guided State Distillation (RGSD) loss.
When incorporated into the optimization-based image-to-3D pipeline, our method
significantly improves 3D generation quality, achieving state-of-the-art
performance. Comprehensive evaluations demonstrate the effectiveness of our
approach over existing methods, both qualitatively and quantitatively. Video
results are available on the project page.
