Abstract
We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approach that produces the visual effect of tessellated geometry while rendering only the original low-polygon meshes. Inspired by our observation from Phong tessellation, NIST leverages the discrepancy between geometric normals and shading normals as a minimal, view-dependent cue for silhouette refinement. At its core, NIST performs multi-scale neural tessellation by progressively deforming image-space contours with convolutional operators, while jointly reassigning appearance information through an implicit warping mechanism to preserve texture coherence and visual fidelity. Experiments demonstrate that our approach produces smooth, visually coherent silhouettes comparable to geometric tessellation, while incurring a constant per-frame cost and fully decoupled from geometric complexity, making it well-suited for large-scale real-time rendering scenarios. To the best of our knowledge, our NIST is the first work to reformulate tessellation as a post-processing operation, shifting it from a pre-rendering geometry pipeline to a screen space neural post-processing stage.
Abstract (translated)
我们介绍了神经图像空间镶嵌（Neural Image-Space Tessellation，NIST），这是一种轻量级的屏幕空间后期处理方法，在仅渲染原始低多边形网格的情况下即可产生几何镶嵌的效果。受到从Phong镶嵌中观察到的现象启发，NIST利用了几何法线和光照法线之间的差异作为最小化、视图依赖的轮廓细化提示符。在核心方面，NIST通过使用卷积操作逐步变形图像空间中的轮廓来执行多尺度神经镶嵌，并通过隐式扭曲机制重新分配外观信息，以保持纹理一致性和视觉保真度。实验表明，我们的方法可以生成平滑且与几何镶嵌相媲美的视觉上连贯的轮廓效果，同时每帧成本固定并且完全独立于几何复杂性，使其非常适合大规模实时渲染场景。据我们所知，NIST是首个将镶嵌重新定义为后期处理操作的工作，将其从预渲染的几何管道转移至屏幕空间神经后期处理阶段。
URL
https://arxiv.org/abs/2602.23754
