FruitNinja：基于高斯泼溅的三维物体内部纹理生成
FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting
AI总结：
本文提出 FruitNinja，首次利用 3DGS 和扩散模型为可切割的三维物体生成内部纹理，实现实时切片渲染，并通过 OpaqueAtom GS 策略提升细粒度纹理质量。
摘要：
在现实世界中，物体在被切开或切割时会显露内部纹理，然而这一行为在当今的三维生成任务中尚未得到充分研究。例如，切开一个虚拟三维西瓜时应当能看到果肉和种子。鉴于目前没有可用的数据集能够捕获物体的完整内部结构，且从所有切片收集数据并不现实，生成式方法成为显而易见的解决途径。然而，现有的三维生成和修复方法往往侧重于可见外观，而忽视了内部纹理。为弥合这一差距，我们提出了 FruitNinja，这是首个为经历几何和拓扑变化的三维物体生成内部纹理的方法。我们的方法通过三维高斯泼溅（3DGS）生成物体，同时合成表面和内部纹理，从而无需额外优化即可实现实时切片和渲染。FruitNinja 利用预训练的扩散模型逐步修复截面视图，并采用基于体素网格的平滑处理，在物体整体范围内实现连贯一致的纹理。我们的 OpaqueAtom GS 策略通过使用密集分布的完全不透明高斯来克服 3DGS 的局限性，避免了对较大粒子的偏倚——这种偏倚会破坏训练稳定性并导致细粒度纹理出现剧烈的颜色跳变。实验结果表明，FruitNinja 显著优于现有方法，在任意几何操作下的实时渲染内部视图中展现出无与伦比的视觉质量。
英文摘要：
In the real world, objects reveal internal textures when sliced or cut, yet this behavior is not well-studied in 3D generation tasks today. For example, slicing a virtual 3D watermelon should reveal flesh and seeds. Given that no available dataset captures an object's full internal structure and collecting data from all slices is impractical, generative methods become the obvious approach. However, current 3D generation and inpainting methods often focus on visible appearance and overlook internal textures. To bridge this gap, we introduce FruitNinja, the first method to generate internal textures for 3D objects undergoing geometric and topological changes. Our approach produces objects via 3D Gaussian Splatting (3DGS) with both surface and interior textures synthesized, enabling real-time slicing and rendering without additional optimization. FruitNinja leverages a pre-trained diffusion model to progressively inpaint cross-sectional views and applies voxel-grid-based smoothing to achieve cohesive textures throughout the object. Our OpaqueAtom GS strategy overcomes 3DGS limitations by employing densely distributed opaque Gaussians, avoiding biases toward larger particles that destabilize training and sharp color transitions for fine-grained textures. Experimental results show that FruitNinja substantially outperforms existing approaches, showcasing unmatched visual quality in real-time rendered internal views across arbitrary geometry manipulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
