InstanceV: Instance-Level Video Generation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914173826367488 |
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| author | Chen, Yuheng Hu, Teng Zhang, Jiangning Xue, Zhucun Yi, Ran Ma, Lizhuang |
| author_facet | Chen, Yuheng Hu, Teng Zhang, Jiangning Xue, Zhucun Yi, Ran Ma, Lizhuang |
| contents | Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video models rely solely on textual conditions, lacking general fine-grained controllability over video generation. To address this challenge, we propose InstanceV, a video generation framework that enables i) instance-level control and ii) global semantic consistency. Specifically, with the aid of proposed Instance-aware Masked Cross-Attention mechanism, InstanceV maximizes the utilization of additional instance-level grounding information to generate correctly attributed instances at designated spatial locations. To improve overall consistency, We introduce the Shared Timestep-Adaptive Prompt Enhancement module, which connects local instances with global semantics in a parameter-efficient manner. Furthermore, we incorporate Spatially-Aware Unconditional Guidance during both training and inference to alleviate the disappearance of small instances. Finally, we propose a new benchmark, named InstanceBench, which combines general video quality metrics with instance-aware metrics for more comprehensive evaluation on instance-level video generation. Extensive experiments demonstrate that InstanceV not only achieves remarkable instance-level controllability in video generation, but also outperforms existing state-of-the-art models in both general quality and instance-aware metrics across qualitative and quantitative evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_23146 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | InstanceV: Instance-Level Video Generation Chen, Yuheng Hu, Teng Zhang, Jiangning Xue, Zhucun Yi, Ran Ma, Lizhuang Computer Vision and Pattern Recognition Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video models rely solely on textual conditions, lacking general fine-grained controllability over video generation. To address this challenge, we propose InstanceV, a video generation framework that enables i) instance-level control and ii) global semantic consistency. Specifically, with the aid of proposed Instance-aware Masked Cross-Attention mechanism, InstanceV maximizes the utilization of additional instance-level grounding information to generate correctly attributed instances at designated spatial locations. To improve overall consistency, We introduce the Shared Timestep-Adaptive Prompt Enhancement module, which connects local instances with global semantics in a parameter-efficient manner. Furthermore, we incorporate Spatially-Aware Unconditional Guidance during both training and inference to alleviate the disappearance of small instances. Finally, we propose a new benchmark, named InstanceBench, which combines general video quality metrics with instance-aware metrics for more comprehensive evaluation on instance-level video generation. Extensive experiments demonstrate that InstanceV not only achieves remarkable instance-level controllability in video generation, but also outperforms existing state-of-the-art models in both general quality and instance-aware metrics across qualitative and quantitative evaluations. |
| title | InstanceV: Instance-Level Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.23146 |