Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation

2026-04-10

AI Summary

This work addresses key limitations in existing 3D texture generation methods—namely incomplete texture coverage, multi-view inconsistency, and poor geometry-texture alignment—by proposing a multi-view-guided native 3D texture generation framework. The approach integrates 2D multi-view generative priors with native 3D representations through two core modules: multi-view synthesis and 3D texture generation. It further incorporates a plug-and-play consistency enhancement mechanism that explicitly optimizes geometric alignment, view consistency, and illumination uniformity while effectively inpainting occluded regions. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in terms of texture detail, fidelity, consistency, and geometric alignment, thereby substantially advancing the quality of 3D texture generation.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Although recent advances have improved the quality of 3D texture generation, existing methods still struggle with incomplete texture coverage, cross-view inconsistency, and misalignment between geometry and texture. To address these limitations, we propose Hitem3D 2.0, a multi-view guided native 3D texture generation framework that enhances texture quality through the integration of 2D multi-view generation priors and native 3D texture representations. Hitem3D 2.0 comprises two key components: a multi-view synthesis framework and a native 3D texture generation model. The multi-view generation is built upon a pre-trained image editing backbone and incorporates plug-and-play modules that explicitly promote geometric alignment, cross-view consistency, and illumination uniformity, thereby enabling the synthesis of high-fidelity multi-view images. Conditioned on the generated views and 3D geometry, the native 3D texture generation model projects multi-view textures onto 3D surfaces while plausibly completing textures in unseen regions. Through the integration of multi-view consistency constraints with native 3D texture modeling, Hitem3D 2.0 significantly improves texture completeness, cross-view coherence, and geometric alignment. Experimental results demonstrate that Hitem3D 2.0 outperforms existing methods in terms of texture detail, fidelity, consistency, coherence, and alignment.

Problem

• 3D texture generation
• texture completeness
• cross-view consistency
• geometry-texture alignment

Innovation

• multi-view consistency
• native 3D texture generation
• geometric alignment
• texture completion
• illumination uniformity