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.

翻译：我们提出神经图像空间细分（NIST），这是一种轻量级的屏幕空间后处理方法，它能在仅渲染原始低多边形网格的同时，产生细分几何体的视觉效果。受Phong细分观察的启发，NIST利用几何法线与着色法线之间的差异作为轮廓细化的最小化、视图相关线索。其核心在于，NIST通过卷积算子逐步变形图像空间轮廓来执行多尺度神经细分，同时通过隐式变形机制联合重新分配外观信息，以保持纹理连贯性和视觉保真度。实验表明，我们的方法能产生与几何细分相媲美的平滑、视觉连贯的轮廓，同时仅需恒定的每帧开销，且完全与几何复杂度解耦，使其非常适合大规模实时渲染场景。据我们所知，我们的NIST是首个将细分量构为后处理操作的工作，将其从预渲染几何管线转移到屏幕空间神经后处理阶段。