Creating realistic and controllable 3D facial avatars is a sig- nificant challenge in computer graphics. While traditional methods are often difficult and time-consuming, emerging technologies like Neural Radiance Fields (NeRFs), Gaussian Splatting (GS), and deepfake meth- ods offer new possibilities. However, a way to combine them to create plausible 3D avatars is still needed. Consequently, we developed Implicit- Deepfake framework to address this gap. It works by first applying a 2D deepfake algorithm to individual training images. Then, a neural ren- dering model (either NeRF or GS) is trained on these altered images. This process allows a consistent 3D avatar to emerge implicitly from the 2D data, avoiding complex direct 3D manipulation. This method can create high-fidelity static and dynamic 3D avatars. We also expand the framework to allow for stylistic and semantic adjustments using diffusion models like Stable Diffusion with ControlNet via text prompts. By com- bining these emerging technologies, our approach provides a functional approach for the next generation of digital avatar creation.