DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909150626185216 |
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| author | Yang, Yunhan Huang, Yukun Wu, Xiaoyang Guo, Yuan-Chen Zhang, Song-Hai Zhao, Hengshuang He, Tong Liu, Xihui |
| author_facet | Yang, Yunhan Huang, Yukun Wu, Xiaoyang Guo, Yuan-Chen Zhang, Song-Hai Zhao, Hengshuang He, Tong Liu, Xihui |
| contents | Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views. In this paper, we present DreamComposer, a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then, it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis, further enhancing them to generate high-fidelity novel view images with multi-view conditions, ready for controllable 3D object reconstruction and various other applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03611 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DreamComposer: Controllable 3D Object Generation via Multi-View Conditions Yang, Yunhan Huang, Yukun Wu, Xiaoyang Guo, Yuan-Chen Zhang, Song-Hai Zhao, Hengshuang He, Tong Liu, Xihui Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views. In this paper, we present DreamComposer, a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then, it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis, further enhancing them to generate high-fidelity novel view images with multi-view conditions, ready for controllable 3D object reconstruction and various other applications. |
| title | DreamComposer: Controllable 3D Object Generation via Multi-View Conditions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.03611 |