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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.14099 |
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| _version_ | 1866918385806213120 |
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| author | Zhu, Ruishu Huang, Zhihao Sun, Jiacheng Luo, Ping Zhang, Hongyuan Li, Xuelong |
| author_facet | Zhu, Ruishu Huang, Zhihao Sun, Jiacheng Luo, Ping Zhang, Hongyuan Li, Xuelong |
| contents | Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view synthesis as a discrete sequence modeling problem where each viewpoint is represented as visual tokens from MAGVIT-v2. Through masked token prediction, our approach enables progressive multi-view generation via iterative token unmasking, unifying language and vision in a shared token space. Importantly, simple random masking combined with self-attention naturally encourages cross-view consistency without specialized architectures or 3D geometric priors. Our method outperforms the baseline on the GSO and 3D-FUTURE benchmarks, ranking first on average across standard image metrics and improving IoU by 10.6% on 3D-FUTURE. This validates discrete diffusion as a promising candidate for multi-view generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Diffusion Models Zhu, Ruishu Huang, Zhihao Sun, Jiacheng Luo, Ping Zhang, Hongyuan Li, Xuelong Computer Vision and Pattern Recognition Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view synthesis as a discrete sequence modeling problem where each viewpoint is represented as visual tokens from MAGVIT-v2. Through masked token prediction, our approach enables progressive multi-view generation via iterative token unmasking, unifying language and vision in a shared token space. Importantly, simple random masking combined with self-attention naturally encourages cross-view consistency without specialized architectures or 3D geometric priors. Our method outperforms the baseline on the GSO and 3D-FUTURE benchmarks, ranking first on average across standard image metrics and improving IoU by 10.6% on 3D-FUTURE. This validates discrete diffusion as a promising candidate for multi-view generation. |
| title | ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.14099 |