Customize-It-3D: High-Quality 3D Creation from A Single Image Using
  Subject-Specific Knowledge Prior
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.
65825ee8afc6b50a2cacfa72
Nan Huang
65825ee8afc6b50a2cacfa73
Ting Zhang
extracted_pending
2023-12-20T03:26:36.433Z
6350ffa18445bbe32e926360
/avatars/bc67d114191be7b16b463731a84c7e05.svg
Ting Zhang
user
hellozt
65825ee8afc6b50a2cacfa74
Yuhui Yuan
extracted_pending
2023-12-20T03:26:36.433Z
631f108bb45367a05fe74260
/avatars/c20da6800b643728062712d9a2771648.svg
Researcher
user
YuanYuhui
65825ee8afc6b50a2cacfa75
Dong Chen
extracted_pending
2023-12-20T03:26:36.433Z
63801921f6d1901fcab032f7
/avatars/fdb1cc08c531c13d5b3bae5efd225bb4.svg
Chen
user
HelloKitty123
65825ee8afc6b50a2cacfa76
Shanghang Zhang
Customize-It-3D: High-Quality 3D Creation from A Single Image Using
  Subject-Specific Knowledge Prior
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.
A subject-specific and multi-modal diffusion model enhances NeRF optimization and texture refinement for image-to-3D generation, producing high-quality 360-degree reconstructions.
diffusion model NeRF shading mode texture refinement faithful reconstruction text-to-3D