NeAR：耦合的神经资产—渲染器栈
NeAR: Coupled Neural Asset-Renderer Stack
把单张随意光照图像经整流流模型提升到光照均匀化的 SLAT 潜空间，再由光照感知神经解码器在 HDR 环境图与相机位姿条件下实时合成可重光照的 3D 高斯泼溅，无需逐物体优化；这套「资产表征与渲染器联合设计」的思路可直接用于单图三维重建与重光照任务。
原文依据 it synthesizes relightable 3D Gaussian splats in real-time without per-object optimization
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论神经资产创作与神经渲染历来作为两条互不相交的范式各自演进：前者为固定的图形管线生成数字资产，后者将传统资产映射为图像。然而，将二者视为彼此独立的实体，限制了在保真度与一致性上进行端到端优化的潜力。本文以 NeAR——一个耦合的神经资产—渲染器栈——弥合这一鸿沟。我们认为，协同设计资产表征与渲染器能够为更优的生成构建一个稳健的「契约」。在资产一侧，我们引入光照均匀化的 SLAT（LH-SLAT）。借助整流流模型，NeAR 将随意光照下的单张图像提升到一个规范的、光照不变的潜空间，从而有效抑制烘焙进图像的阴影与高光。在渲染器一侧，我们设计了一个光照感知的神经解码器，专门用于解读这些均匀化的潜变量。以 HDR 环境图与相机视角为条件，该解码器能够实时合成可重光照的 3D 高斯泼溅，且无需逐物体优化。我们在四项任务上验证了 NeAR：（1）基于 G-buffer 的前向渲染，（2）随机光照下的重建，（3）未知光照下的重光照，（4）新视角重光照。大量实验表明，我们的耦合栈在定量指标与感知质量上均优于最优基线。我们希望这种耦合的资产—渲染器视角能够启发未来的图形栈，将神经资产与渲染器视为协同设计的组件，而非彼此独立的实体。
英文原文摘要 1539 字符
Neural asset authoring and neural rendering have traditionally evolved as disjoint paradigms: one generates digital assets for fixed graphics pipelines, while the other maps conventional assets to images. However, treating them as independent entities limits the potential for end-to-end optimization in fidelity and consistency. In this paper, we bridge this gap with NeAR, a Coupled Neural Asset--Renderer Stack. We argue that co-designing the asset representation and the renderer creates a robust "contract" for superior generation. On the asset side, we introduce the Lighting-Homogenized SLAT (LH-SLAT). Leveraging a rectified-flow model, NeAR lifts casually lit single images into a canonical, illumination-invariant latent space, effectively suppressing baked-in shadows and highlights. On the renderer side, we design a lighting-aware neural decoder tailored to interpret these homogenized latents. Conditioned on HDR environment maps and camera views, it synthesizes relightable 3D Gaussian splats in real-time without per-object optimization. We validate NeAR on four tasks: (1) G-buffer-based forward rendering, (2) random-lit reconstruction, (3) unknown-lit relighting, and (4) novel-view relighting. Extensive experiments demonstrate that our coupled stack outperforms state-of-the-art baselines in both quantitative metrics and perceptual quality. We hope this coupled asset-renderer perspective inspires future graphics stacks that view neural assets and renderers as co-designed components instead of independent entities.