Creating high-fidelity head avatars from multi-view videos is essential for many AR/VR applications. However, current methods often struggle to achieve high-quality renderings across all head components (e.g., skin vs. hair) due to the limitations of using one single representation for elements with varying characteristics. In this paper, we introduce a Hybrid Mesh-Gaussian Head Avatar (MeGA) that models different head components with more suitable representations. Specifically, we employ an enhanced FLAME mesh for the facial representation and predict a UV displacement map to provide per-vertex offsets for improved personalized geometric details. To achieve photorealistic rendering, we use deferred neural rendering to obtain facial colors and decompose neural textures into three meaningful parts. For hair modeling, we first build a static canonical hair using 3D Gaussian Splatting. A rigid transformation and an MLP-based deformation field are further applied to handle complex dynamic expressions. Combined with our occlusion-aware blending, MeGA generates higher-fidelity renderings for the whole head and naturally supports diverse downstream tasks. Experiments on the NeRSemble dataset validate the effectiveness of our designs, outperforming previous state-of-the-art methods and enabling versatile editing capabilities, including hairstyle alteration and texture editing. The code is released in https://github.com/conallwang/MeGA.

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of con-trollable high-quality image generation from arbitrary text prompts. However, the generation quality and general-ization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper, we present PI3D, a framework that fully lever-ages the pre-trained text-to-image diffusion models' abil-ity to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image dif-fusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly, we find that it can al-ready generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative re-finement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.