OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911322420019200 |
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| author | Tang, Tao Ma, Enhui zhou, xia Wang, Letian Yan, Tianyi Zhang, Xueyang Zhan, Kun Jia, Peng Lang, XianPeng Bian, Jia-Wang Yu, Kaicheng Liang, Xiaodan |
| author_facet | Tang, Tao Ma, Enhui zhou, xia Wang, Letian Yan, Tianyi Zhang, Xueyang Zhan, Kun Jia, Peng Lang, XianPeng Bian, Jia-Wang Yu, Kaicheng Liang, Xiaodan |
| contents | Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inefficient. Generative models have emerged as a promising solution by synthesizing realistic sensor data. However, existing approaches primarily focus on single-modality generation, leading to inefficiencies and misalignment in multimodal sensor data. To address these challenges, we propose OminiGen, which generates aligned multimodal sensor data in a unified framework. Our approach leverages a shared Bird\u2019s Eye View (BEV) space to unify multimodal features and designs a novel generalizable multimodal reconstruction method, UAE, to jointly decode LiDAR and multi-view camera data. UAE achieves multimodal sensor decoding through volume rendering, enabling accurate and flexible reconstruction. Furthermore, we incorporate a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation. Our comprehensive experiments demonstrate that OminiGen achieves desired performances in unified multimodal sensor data generation with multimodal consistency and flexible sensor adjustments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving Tang, Tao Ma, Enhui zhou, xia Wang, Letian Yan, Tianyi Zhang, Xueyang Zhan, Kun Jia, Peng Lang, XianPeng Bian, Jia-Wang Yu, Kaicheng Liang, Xiaodan Computer Vision and Pattern Recognition Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inefficient. Generative models have emerged as a promising solution by synthesizing realistic sensor data. However, existing approaches primarily focus on single-modality generation, leading to inefficiencies and misalignment in multimodal sensor data. To address these challenges, we propose OminiGen, which generates aligned multimodal sensor data in a unified framework. Our approach leverages a shared Bird\u2019s Eye View (BEV) space to unify multimodal features and designs a novel generalizable multimodal reconstruction method, UAE, to jointly decode LiDAR and multi-view camera data. UAE achieves multimodal sensor decoding through volume rendering, enabling accurate and flexible reconstruction. Furthermore, we incorporate a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation. Our comprehensive experiments demonstrate that OminiGen achieves desired performances in unified multimodal sensor data generation with multimodal consistency and flexible sensor adjustments. |
| title | OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.14225 |