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InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting

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arxiv 2403.11878 v1 pith:ESEQB27T submitted 2024-03-18 cs.CV

classification cs.CV
keywords synthesisinpaintingtext-to-texturedepth-awareintexcontentcreationframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models. Existing methods primarily adopt a combination of pretrained depth-aware diffusion and inpainting models, yet they exhibit shortcomings such as 3D inconsistency and limited controllability. To address these challenges, we introduce InteX, a novel framework for interactive text-to-texture synthesis. 1) InteX includes a user-friendly interface that facilitates interaction and control throughout the synthesis process, enabling region-specific repainting and precise texture editing. 2) Additionally, we develop a unified depth-aware inpainting model that integrates depth information with inpainting cues, effectively mitigating 3D inconsistencies and improving generation speed. Through extensive experiments, our framework has proven to be both practical and effective in text-to-texture synthesis, paving the way for high-quality 3D content creation.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TexTailor: Customized Text-aligned Texturing via Effective Resampling

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A diffusion-based texturing method that uses DDIM resampling, per-object ControlNet fine-tuning with a preservation loss, and adaptive camera placement to reduce view-to-view texture drift.

  2. A Scalable Attention-Based Approach for Image-to-3D Texture Mapping

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A DINOv2-conditioned transformer decodes a triplane texture field on a given mesh, supervised by depth backprojection, producing UV-free textures in 0.2 s per shape.

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