Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing

We present a novel framework for generating photorealistic Editing capabilities for 3D-aware GANs have also been 3D human head and subsequently manipulating achieved through latent space auto-decoding, altering a 2D and reposing them with remarkable flexibility. The proposed semantic segmentation [62, 63], or modifying the underlying approach leverages an implicit function representation geometry scaffold [64]. However, generation and editing of 3D human heads, employing 3D Gaussians anchored quality tends to be unstable and less diversified due to on a parametric face model. To enhance representational the inherent limitation of GANs, and detailed-level editing capabilities and encode spatial information, we is not well supported due to feature entanglement in the embed a lightweight tri-plane payload within each Gaussian compact latent space or tri-plane representations.