Neural Dynamic GI: Random-Access Neural Compression for Temporal Lightmaps in Dynamic Lighting Environments Jianhui Wu, Jian Zhou, Zhi Zhou, Zhangjin Huang, Chao Li arxiv.org/abs/2604.12625 Abstract: High-quality global illumination (GI) in real-time rendering is commonly achieved using precomputed lighting techniques, with lightmap as the standard choice. To support GI for static objects in dynamic lighting environments, multiple lightmaps at different lighting conditions need to be precomputed, which incurs substantial storage and memory overhead. To overcome this limitation, we propose Neural Dynamic GI (NDGI), a novel compression technique specifically designed for temporal lightmap sets. Our method utilizes multi-dimensional feature maps and lightweight neural networks to integrate the temporal information instead of storing multiple sets explicitly, which significantly reduces the storage size of lightmaps. Additionally, we introduce a block compression (BC) simulation strategy during the training process, which enables BC compression on the final generated feature maps and further improves the compression ratio. To enable efficient real-time decompression, we also integrate a virtual texturing (VT) system with our neural representation. Compared with prior methods, our approach achieves high-quality dynamic GI while maintaining remarkably low storage and memory requirements, with only modest real-time decompression overhead. To facilitate further research in this direction, we will release our temporal lightmap dataset precomputed in multiple scenes featuring diverse temporal variations.

Neural Caching of Prefiltered Radiance for Specular Lighting Dmitrii Klepikov, Vladimir Frolov arxiv.org/abs/2610.11702 Abstract: Neural Radiance Caching (NRC) provides an online neural representation of scene illumination for real-time path tracing. This paper presents an NRC variant tailored to specular lighting through a reflection-direction parameterization and a roughness-dependent radiance target. Our network predicts prefiltered incoming radiance at individual surface points, which is combined with a precomputed BRDF integration map using the split-sum approximation to estimate outgoing radiance. On Bunny and Specular Sponza, the reported comparisons indicate faster convergence to the accumulated path-tracing reference and improved specular-lighting quality relative to the evaluated NRC baselines. Our online-trained method maintains real-time performance while adapting its cache during rendering.

Real-time Rendering of Pre-integrated Neural Emitters Arno Coomans, Floor Verhoeven, Edoardo A. Dominici, Markus Steinberger (Graz University of Technology, Austria; Huawei Technologies, Switzerland2, Austria) arxiv.org/abs/2610.06762 Abstract: Direct illumination from non-trivial emitters with occluding housing, high-polygon emissive meshes, spatially varying emission, or deformable assemblies is a persistent bottleneck in real-time rendering. Current real-time solutions either rely on simplified analytical representations or fall back to runtime sampling. Analytical methods are restricted to simple emitter geometry, pure sampling-based estimators need high sampling budgets to be noise-free, and proxy representations still require runtime integration over outgoing radiance around the emitter. We present Neural Emission Fields (NEF), a representation that eliminates runtime integration by precomputing the illumination in the volume around the emitter. This neural field is parameterized by position, normal, view direction, and material parameters. Using a two-headed diffuse/glossy architecture, it yields noise-free unoccluded direct illumination from a single network evaluation per shading point. Because training is performed in the emitter's local frame, a trained NEF acts as a portable lighting asset reusable across scenes under rigid transforms. Internal interreflections, self-occlusion, spatially varying emission, and deformations are absorbed into the learned representation at zero additional runtime cost.

Physically Based Rendering in the Latent Space Vuk Radovanovic, Vishesh Gupta, Adrien Gruson, Binh-Son Hua Paper: arxiv.org/abs/2609.21054 Code (MIT? Soon): github.com/trinity-graphics/… Abstract: Image diffusion models have shown impressive image generation capabilities but are often hard to control, in contrast to classical computer graphics pipelines such as physically based rendering. However, we observe that there is a bridge between light transport phenomena and the distribution of latent space values produced by such models. Thus, we introduce physically based rendering in the feature space learned by the variational autoencoders in generative models, enabling light transport simulation in the latent space. This allows us to leverage physically based rendering techniques to output latent maps for physically guided content generation. We propose modifications to the rendering equation, which, when paired with a differentiable renderer, can yield an optimal set of scene parameters that require only minimal refinement to accurately render into the pretrained latent space. We train our method on a single rendered image, and then demonstrate the generalization of the method to scene geometry changes, lighting changes, and camera view changes.