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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.06938 |
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| _version_ | 1866917719079649280 |
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| author | Zhang, Bowen Cheng, Yiji Wang, Chunyu Zhang, Ting Yang, Jiaolong Tang, Yansong Zhao, Feng Chen, Dong Guo, Baining |
| author_facet | Zhang, Bowen Cheng, Yiji Wang, Chunyu Zhang, Ting Yang, Jiaolong Tang, Yansong Zhao, Feng Chen, Dong Guo, Baining |
| contents | We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image. Existing methods fail to capture intricate details such as hairstyles which we tackle in this paper. We first identify an overlooked problem of catastrophic forgetting that arises when fitting triplanes sequentially on many avatars, caused by the MLP decoder sharing scheme. To overcome this issue, we raise a novel data scheduling strategy and a weight consolidation regularization term, which improves the decoder's capability of rendering sharper details. Additionally, we optimize the guiding effect of the portrait image by computing a finer-grained hierarchical representation that captures rich 2D texture cues, and injecting them to the 3D diffusion model at multiple layers via cross-attention. When trained on 46K avatars with a noise schedule optimized for triplanes, the resulting model can generate 3D avatars with notably better details than previous methods and can generalize to in-the-wild portrait input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models Zhang, Bowen Cheng, Yiji Wang, Chunyu Zhang, Ting Yang, Jiaolong Tang, Yansong Zhao, Feng Chen, Dong Guo, Baining Computer Vision and Pattern Recognition We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image. Existing methods fail to capture intricate details such as hairstyles which we tackle in this paper. We first identify an overlooked problem of catastrophic forgetting that arises when fitting triplanes sequentially on many avatars, caused by the MLP decoder sharing scheme. To overcome this issue, we raise a novel data scheduling strategy and a weight consolidation regularization term, which improves the decoder's capability of rendering sharper details. Additionally, we optimize the guiding effect of the portrait image by computing a finer-grained hierarchical representation that captures rich 2D texture cues, and injecting them to the 3D diffusion model at multiple layers via cross-attention. When trained on 46K avatars with a noise schedule optimized for triplanes, the resulting model can generate 3D avatars with notably better details than previous methods and can generalize to in-the-wild portrait input. |
| title | RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.06938 |