OccamSplat: Learning Appearance Capacity for Dynamic Gaussian Splatting

Capturing view- and time-dependent appearance with explicit Gaussian models can be costly when every primitive receives a full angular–temporal basis. Gaussians contribute unequally to rendered images, and their needs for directional and temporal variation differ. Uniform allocation therefore spends storage and computation on capacity that may offer little reconstruction benefit. The challenge is to identify useful extensions before fitting them: motion and residual magnitude are indirect cues, and updates to different Gaussians can interact. We introduce OccamSplat, which selects appearance capacity by verifying the rendering benefit of a concrete joint update before activation. Starting from base color, we probe candidate four-dimensional spherical-harmonic (4DSH) bands with zero coefficients, preserving the current rendering. Cross-observation gradient agreement yields bounded coefficient updates, which are accepted only when joint rendering reduces the loss on two disjoint sampled observation sets not used to construct them. We evaluate on Neural 3D Video, MeetRoom, and Technicolor light-field. On Neural 3D Video, under the same backbone and densification configuration, our method improves PSNR over Uniform 4DSH by 0.60 dB with 12.8% of non-DC coefficient rows active, while rendering at 257 FPS.