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InteX: Interactive Text-to-Texture Synthesis Framework with Unified Depth-aware Inpainting


Core Concepts
InteX introduces an interactive text-to-texture synthesis framework with unified depth-aware inpainting, enhancing controllability and efficiency in 3D content creation.
Abstract
Introduction: Text-to-texture synthesis addresses challenges in creating high-quality textures for 3D objects. Recent advances in denoising diffusion models have improved text-to-image synthesis tasks. Existing Methods: Two main approaches: direct training of 3D diffusion models and leveraging pretrained 2D diffusion models. Challenges include blurriness, limited diversity, and 3D inconsistency in texture generation. InteX Framework: User-friendly interface for interactive visualization, inpainting, and repainting of textures. Unified depth-aware inpainting model improves 3D consistency and generation speed. Methodology: Unified Depth-aware Inpainting Prior Model: Architecture based on ControlNet for inpainting guided by text prompts and depth information. Training on Objaverse dataset with dynamic mask generation strategy. Iterative Texture Synthesis: Utilizes depth-aware inpainting prior model for efficient texture synthesis on 3D surfaces. Rendering, inpainting, and updating process explained in detail. GUI for Practical Use: Graphic User Interface enhances user interaction by allowing viewpoint selection, erasing unwanted areas, and changing text prompts during generation. Experiments: Effectiveness of depth-aware inpainting demonstrated through comparison with baseline methods. Qualitative comparisons show superior performance in texture quality and 3D consistency. Ablation Study: Diffusion priors comparison highlights the importance of unified depth-aware inpainting model. Comparison between auto-generated UV maps and artist-created UV maps showcases satisfactory results with both approaches. Limitations: Single-view rendering may lead to 3D inconsistencies without suitable camera choices. Conclusion: InteX offers a practical solution for text-to-texture synthesis with enhanced controllability, efficiency, and quality in generating high-quality textures for 3D content creation.
Stats
"Through extensive experiments, our framework has proven to be both practical and effective." "Our method stands out for its enhanced controllability, efficiency, and flexibility."
Quotes
"Our approach also alleviates the challenges of 3D consistency and enhances generation speed in text-to-texture synthesis." "Users are provided with unparalleled control over the texture synthesis process."

Key Insights Distilled From

by Jiaxiang Tan... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11878.pdf
InTeX

Deeper Inquiries

How can multi-view diffusion models address the issue of 3D inconsistency

マルチビュー拡散モデルは、3Dの不整合性の問題にどのように対処できるかを示唆しています。従来の単一視点から生成されたテクスチャでは、特定の領域が正確に表現されず、立体感や一貫性が欠けることがあります。しかし、マルチビュー拡散モデルを使用することで、複数の視点から得られた情報を組み合わせてテクスチャを生成することが可能です。これにより、異なる角度から得られた情報を総合的に利用し、3Dオブジェクト全体にわたって一貫したテクスチャ生成が実現されます。

What are the implications of using different diffusion priors on the overall texture quality

異なる拡散先行モデルを使用することが全体的なテクスチャ品質に与える影響は重要です。例えば、「深さだけ」モデルでは深さ情報だけで画像生成を行い、「インペイントだけ」モデルでは修復技術だけで画像生成を行います。これらの異なるアプローチはそれぞれ独自の長所と制約があります。深さ情報も修復技術も組み合わせた「統合型深度・修復」モデルは両方の利点を活かしつつ、高品質かつ一貫性あるテクスチャ生成を可能にします。

How can advancements in multi-view diffusion models enhance the practical utility of texture synthesis frameworks

マルチビュー拡散モデルへの進歩は、テクスチャ合成フレームワークの実用性向上にどう影響するか考察すべきです。この進化した手法では複数視点から得られた情報や多様な角度から捉えられたコンテンツが反映されます。その結果、よりリアリズム満ちた高品質な3Dコンテンツや滑らかな遷移効果が期待されます。また、新しい手法は柔軟性や操作性も向上しました。「InteX」という架空フレームワークでも見られるようにGUI(グラフィカル・ ユーザ・ インターフェース) を通じて直感的操作や精密な編集機能も提供します。
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