Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline

• URL: http://arxiv.org/abs/2609.07085v1
• Date: Mon, 07 Sep 2026 06:16:43 GMT
• Title: Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline
• Abstract summary: We propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild.<n>Under this framework, we further propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild.
• Score: 13.122536259577453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion models pre-trained on large-scale datasets served as 2D priors are used to solve the ill-posed task but suffer from color deviation and view inconsistency, which can be curbed by using diffusion models fine-tuned with 3D annotated data served as 3D priors. However, 3D priors lack high-frequency details, which cannot be solved by direct complementation with 2D priors in spatial domain for introducing erroneous low-frequency 2D prior guidance. In this paper, we revisit the characteristics of different diffusion priors from the frequency perspective. Based on our observations, we theoretically present a unified framework of hybrid optimization using multiple diffusion priors in frequency domain. Under this framework, we further propose Morpheus3D, a pipeline of 3D object generation from any single unposed image in the wild. Morpheus3D enhances 3D prior with high-pass image-prompt 2D prior guidance to reconstruct high-quality 3D objects while effectively suppressing view inconsistency, low-frequency color deviation, and high-frequency lacking problems. Both quantitative and qualitative experiments on the public and our collected datasets with complex textures show that our method exhibits significant improvements in generation quality.

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