StrokeFaceNeRF: Stroke-Based Facial Appearance Editing in Neural Radiance Field

Current 3D-aware facial NeRF generation approaches control the facial appearance by text, lighting conditions or reference images, limiting precise manipulation of local facial regions and interactivity. Color stroke, a user-friendly and effective tool to depict appearance, is challenging to edit 3D faces because of the lack of texture, coarse geometry representation and detailed editing operations. To solve the above problems, we introduce StrokeFaceNeRF, a novel stroke-based method for editing facial NeRF appearance. In order to infer the missing texture and 3D geometry information, 2D edited stroke maps are firstly encoded into the EG3D's latent space, followed by a transformer-based editing module to achieve effective appearance changes while preserving the original geometry in editing regions. Notably, we design a novel geometry loss function to ensure surface density remains consistent during training. To further enhance the local manipulation accuracy, we propose a stereo fusion approach which lifts the 2D mask (inferred from strokes or drawn by users) into 3D mask volume, allowing explicit blending of the original and edited faces. Extensive experiments validate that the proposed method outperforms existing 2D and 3D methods in both editing reality and geometry retention.