Neural Image Space Tessellation efect 文章

ArXiv CS.CV2026-05-28NEWSen作者: Youyang Du (Shandong University, Mohamed bin Zayed University of Artificial Intelligence), Junqiu Zhu (Shandong University), Zheng Zeng (University of California, Santa Barbara), Lu Wang (Shandong University), Lingqi Yan (Mohamed bin Zayed University of Artificial Intelligence)

摘要

arXiv:2602.23754v2 Announce Type: replace-cross 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.

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Neural Image Space Tessellation efect
2026-05-28PRODUCT_LAUNCH影响: MEDIUM

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