  Recent advancements in automatic 3D avatar generation guided by text have
made significant progress. However, existing methods have limitations such as
oversaturation and low-quality output. To address these challenges, we propose
X-Oscar, a progressive framework for generating high-quality animatable avatars
from text prompts. It follows a sequential Geometry->Texture->Animation
paradigm, simplifying optimization through step-by-step generation. To tackle
oversaturation, we introduce Adaptive Variational Parameter (AVP), representing
avatars as an adaptive distribution during training. Additionally, we present
Avatar-aware Score Distillation Sampling (ASDS), a novel technique that
incorporates avatar-aware noise into rendered images for improved generation
quality during optimization. Extensive evaluations confirm the superiority of
X-Oscar over existing text-to-3D and text-to-avatar approaches. Our anonymous
project page: https://xmu-xiaoma666.github.io/Projects/X-Oscar/.
