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In this paper, we present a novel two-stage approach that fully utilizes theinformation provided by the reference image to establish a customized knowledgeprior for image-to-3D generation. While previous approaches primarily rely on ageneral diffusion prior, which struggles to yield consistent results with thereference image, we propose a subject-specific and multi-modal diffusion model.This model not only aids NeRF optimization by considering the shading mode forimproved geometry but also enhances texture from the coarse results to achievesuperior refinement. Both aspects contribute to faithfully aligning the 3Dcontent with the subject. Extensive experiments showcase the superiority of ourmethod, 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.

Customize-It-3D: High-Quality 3D Creation from A Single Image Using Subject-Specific Knowledge Prior