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Coarse3D model generates controllable and faithful sparse 3D structures from images

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Coarse3D, a novel model designed to generate controllable and faithful sparse 3D structures from single images. This approach addresses limitations in existing coarse-stage generators, which struggle with ambiguous semantic canonicalization for orientation control and inconsistent structures due to weak 2D-3D grounding. Coarse3D employs a first-frame anchored canonical space, a dual-tower Diffusion Transformer (DiT) with auxiliary reconstruction, and spatial-prior guided attention to improve orientation control and structural consistency. The model can be integrated into existing pipelines, demonstrating state-of-the-art performance by enhancing final 3D generation quality. AI

IMPACT Introduces a new method for improving the initial stages of image-to-3D generation, potentially enhancing the quality and controllability of 3D asset creation.

RANK_REASON Academic paper detailing a new model and its technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Coarse3D model generates controllable and faithful sparse 3D structures from images

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Academic paper detailing a new model and its technical approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Gaige Wang, Bowen Pan, Hao Ren, Jiangjing Lyu, Chengfei Lyu ·

    Coarse3D: Controllable and Faithful Sparse Structure Generation for Image-to-3D

    arXiv:2610.41231v1 Announce Type: new Abstract: Coarse-to-fine 3D-native generation relies heavily on the first-stage sparse structure, which determines the global orientation and overall geometry for subsequent refinement. However, existing coarse generators suffer from two key …

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