  In this paper, we present a novel two-stage approach that fully utilizes the
information provided by the reference image to establish a customized knowledge
prior for image-to-3D generation. While previous approaches primarily rely on a
general diffusion prior, which struggles to yield consistent results with the
reference image, we propose a subject-specific and multi-modal diffusion model.
This model not only aids NeRF optimization by considering the shading mode for
improved geometry but also enhances texture from the coarse results to achieve
superior refinement. Both aspects contribute to faithfully aligning the 3D
content with the subject. Extensive experiments showcase the superiority of our
method, Customize-It-3D, outperforming previous works by a substantial margin.
It produces faithful 360-degree reconstructions with impressive visual quality,
making it well-suited for various applications, including text-to-3D creation.
