แนวคิดหลัก
InteX introduces an interactive text-to-texture synthesis framework with unified depth-aware inpainting, enhancing controllability and efficiency in 3D content creation.
บทคัดย่อ
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.
สถิติ
"Through extensive experiments, our framework has proven to be both practical and effective."
"Our method stands out for its enhanced controllability, efficiency, and flexibility."
คำพูด
"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."