X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation
<|reference_start|>X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation: 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/.<|reference_end|>
@article{ma2024x-oscar:,
    title={X-Oscar: A Progressive Framework for High-quality Text-guided 3D
  Animatable Avatar Generation},
    author={Yiwei Ma, Zhekai Lin, Jiayi Ji, Yijun Fan, Xiaoshuai Sun, Rongrong Ji},
    journal={arXiv preprint arXiv:2405.00954},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00954},
    primaryClass={cs.CV}
}