COSY: Compositional 3DGS Synthesis for Disentangled Human Head Editing

Recent 3D Gaussian Splatting (3DGS) GANs for humanheads synthesize and render photorealistic 3D models in real-time andoffer a vast variety in identity and appearance. However, controlling spe-cific semantic attributes such as hair color or glasses remains challenging,as edits in the entangled latent space often induce unintended changesin identity or appearance. Although there are several methods that aimto disentangle the latent space post training by estimating directionsthat only modify certain features, these methods cannot guarantee com-plete disentanglement and often require pre-trained classifiers. In ourapproach, we propose a new generator architecture that synthesizes com-ponents, such as hair, skin, glasses, and torso, completely independently.This allows for changing the latent vector for one region while keepingthe remaining parts fixed. Further, we achieve this separation using onlysparse information such as the hair or skin color, eliminating the re-quirement of segmentation masks or geometric priors, often seen in priorwork. To ensure matching shape and lighting conditions during editing,we allow minimal shared information via context tokens between the in-dependent generators. These tokens even allow us to control the shapeand light, without any prior annotation. Compared to existing works onGAN-based generation and editing, our method shows better disentan-glement, more precise editing control, and competitive visual quality.