Lightmap-GS: Neural Compression of Dynamic Lightmaps via 2D Gaussians

Dynamic HDR lightmaps store changing illumination on fixed, sparsely occupied UV atlases. Their compression requires spatial capacity to follow atlas detail while sharing information across lighting states. We present \method, a content-specific representation that couples fixed anisotropic 2D Gaussian kernels with a factorized temporal color field. A multiresolution TriPlane decoder predicts color residuals, and tile-local normalized Top-$K$ reconstruction supports full-frame decoding and direct texel evaluation. Capacity-controlled ablations show gains of 2.17 dB from factorized features, 1.30 dB from residual color prediction, and 1.27 dB from adaptive allocation in the tested configurations. On 40 synthetic lightmap sequences, \method achieves 35.41 dB under a shared-scale evaluation protocol. A twelve-state, $512\times 1024$ sequence occupies approximately 0.41 MiB versus 72 MiB in Float32, with 1.96 ms median full-frame reconstruction on an RTX 4090. Additional lighting states, external assets, and an Unreal Engine integration characterize temporal coverage and deployment costs. A matched-storage diagnostic improves the weakest fitted-state quality over temporal Gaussian alternatives, while higher-precision HEVC achieves substantially better full-atlas fidelity. Optimized neural and hardware-video comparisons further motivate evaluating selective texel access separately from bulk reconstruction and rate–distortion performance.