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.

翻译：在现实世界中，物体在被切割或剖开时会显露内部纹理，然而这种行为在当前的3D生成任务中尚未得到充分研究。例如，切分一个虚拟的3D西瓜应能展现果肉与籽粒。鉴于现有数据集均未完整捕获物体的内部结构，且从所有切面收集数据不切实际，生成式方法成为必然选择。然而，当前的3D生成与修复方法通常关注可见外观而忽略了内部纹理。为填补这一空白，我们提出了FruitNinja，这是首个能为经历几何与拓扑变化的3D物体生成内部纹理的方法。我们的方法通过3D高斯溅射（3DGS）生成同时具备表面与内部纹理的物体，无需额外优化即可实现实时切割与渲染。FruitNinja利用预训练的扩散模型逐步修复截面视图，并应用基于体素网格的平滑处理以实现物体整体纹理的连贯性。我们提出的OpaqueAtom GS策略通过采用密集分布的不透明高斯粒子，克服了3DGS的局限性，避免了训练过程中因偏向大粒子而导致的不稳定问题，并为细粒度纹理实现了锐利的色彩过渡。实验结果表明，FruitNinja显著优于现有方法，在任意几何操作下实时渲染的内部视图中展现出无可匹敌的视觉质量。