Authors: Youyang Du, Junqiu Zhu, Zheng Zeng, Lu Wang, Lingqi Yan
Organizations: Shandong University · Mohamed bin Zayed University of Artificial Intelligence · Shandong University, China · Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates · University of California, Santa Barbara · University of California, Santa Barbara, USA
Abstract
We present Neural Image Space Tessellation effect (NIST), a lightweight screen-space post-processing approach for reducing the faceted silhouettes of low-poly renderings. Instead of tessellating primitives, creating new geometry, or modifying the underlying mesh, NIST uses the low-poly rendering result together with simple auxiliary G-buffer attributes to learn geometry-guided smoothing of object contours in image space. At its core, NIST first deforms image-space contours implicitly and then learns to reassign appearance in the whole image-space, including the deformed regions, preserving texture continuity and avoiding seam artifacts. Experiments show that NIST reduces visually apparent geometric faceting and produces smooth, coherent silhouettes close to tessellation-based smoothing references, with a nearly constant per-frame cost in our tested settings. To the best of our knowledge, NIST is the first work to move the solution of low-poly silhouette faceting from the pre-rendering geometry stage to a post-rendering screen-space stage.