Recent advances in text-to-image diffusion models have enabled 3D generation
from a single image. However, current image-to-3D 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 technique that substantially reduces
the quality gap between synthesized and reference views. Second, capitalizing
on the novel view enhancement, we present a novel reference-guided state
distillation 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.

本文介绍了 HiFi-123，一种用于高保真度和多视角一致的 3D 生成的方法，通过引入参考引导的新视图增强技术和引导状态蒸馏损失，在优化的图像到 3D 过程中显著提高了 3D 生成的质量，取得了最先进的性能。通过全面的评估，定性和定量地证明了我们方法的有效性。