X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913339352809472 |
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| author | Ma, Yiwei Lin, Zhekai Ji, Jiayi Fan, Yijun Sun, Xiaoshuai Ji, Rongrong |
| author_facet | Ma, Yiwei Lin, Zhekai Ji, Jiayi Fan, Yijun Sun, Xiaoshuai Ji, Rongrong |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_00954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation Ma, Yiwei Lin, Zhekai Ji, Jiayi Fan, Yijun Sun, Xiaoshuai Ji, Rongrong Computer Vision and Pattern Recognition 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/. |
| title | X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.00954 |