LivingAvatars: Robust Head Reconstruction With Gaussian Lifecycle Management and Neural Detail Synthesis

Creating robust, high-fidelity head avatars with 3D Gaussian Splatting (3DGS) remains constrained by two bottlenecks: one is the representation bottleneck that limits the modeling of dynamic details, and the other is the dynamic robustness bottleneck that causes information loss and point cloud inflation. To this end, we present LivingAvatars, which addresses both issues through two key designs: (i) a functionally decoupled Gaussian representation that separates identity-stable base Gaussians from detail Gaussians carrying high-frequency appearance, while a lightweight neural decoder injects features into the latter to synthesize expression-dependent details; and (ii) a Gaussian lifecycle management system that endows Gaussians with multiple states and replaces destructive pruning with probabilistic selective pruning and temporary hiding to suppress redundant growth, ensuring a compact and robust representation. On novel-view synthesis and self-reenactment, LivingAvatars achieves overall better metrics than existing methods. In cross-identity reenactment, it better preserves transient, expression-dependent signals, yielding higher perceptual fidelity. These results suggest that functionally decoupled Gaussian representations and lifecycle management are effective principles for building neural head avatars that remain robust and compact under dynamic occlusions, reliably reconstructing high-frequency details and improving overall realism.