One Avatar, Any Budget: Robust LoD for Dynamic Gaussian Avatars

Cross-device deployment of a single reconstructed avatar often requires adaptation to varying Gaussian budgets with minimal degradation in visual quality. Motivated by the need for a robust budget-adaptive 3D representation, we propose RobustAvatar, a train-once Level-of-Detail (LoD) adaptation framework for dynamic Gaussian avatars based on sequential selection and compensation. RobustAvatar supports flexible compression-ratio control within a single trained avatar, without requiring fine-tuning at each compression ratio. The framework consists of two coordinated modules: Semantic Distribution-Regularized Selection (SDS) and Canonical Budget-Conditioned Compensation (CBC). SDS performs budget-aware Gaussian selection guided by semantic distribution regularization, preserving the coverage of perceptually salient facial regions relative to the full avatar under constrained budgets. CBC refines the selected primitives in a canonical, expression- and pose-neutral space by predicting ratio-conditioned attribute residuals, reducing detail loss from Gaussian removal and improving robustness to decreasing Gaussian budgets. Experiments on the NeRSemble dataset show that RobustAvatar achieves more robust budget adaptation than state-of-the-art methods, with slower quality degradation as Gaussian budgets decrease. The code will be made publicly available.