FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908769304182784 |
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| author | Huang, Xijie Xu, Chengming Luo, Donghao Hu, Xiaobin Tang, Peng Peng, Xu Zhang, Jiangning Wang, Chengjie Fu, Yanwei |
| author_facet | Huang, Xijie Xu, Chengming Luo, Donghao Hu, Xiaobin Tang, Peng Peng, Xu Zhang, Jiangning Wang, Chengjie Fu, Yanwei |
| contents | First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identify the root cause of this limitation as the inadequacy of current training datasets, which are often too short, low-resolution, and lack the task diversity required to teach robust temporal priors. To address this foundational data gap, we first introduce FFP-300K, a new large-scale dataset comprising 300K high-fidelity video pairs at 720p resolution and 81 frames in length, constructed via a principled two-track pipeline for diverse local and global edits. Building on this dataset, we propose a novel framework designed for true guidance-free FFP that resolves the critical tension between maintaining first-frame appearance and preserving source video motion. Architecturally, we introduce Adaptive Spatio-Temporal RoPE (AST-RoPE), which dynamically remaps positional encodings to disentangle appearance and motion references. At the objective level, we employ a self-distillation strategy where an identity propagation task acts as a powerful regularizer, ensuring long-term temporal stability and preventing semantic drift. Comprehensive experiments on the EditVerseBench benchmark demonstrate that our method significantly outperforming existing academic and commercial models by receiving about 0.2 PickScore and 0.3 VLM score improvement against these competitors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01720 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing Huang, Xijie Xu, Chengming Luo, Donghao Hu, Xiaobin Tang, Peng Peng, Xu Zhang, Jiangning Wang, Chengjie Fu, Yanwei Computer Vision and Pattern Recognition First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identify the root cause of this limitation as the inadequacy of current training datasets, which are often too short, low-resolution, and lack the task diversity required to teach robust temporal priors. To address this foundational data gap, we first introduce FFP-300K, a new large-scale dataset comprising 300K high-fidelity video pairs at 720p resolution and 81 frames in length, constructed via a principled two-track pipeline for diverse local and global edits. Building on this dataset, we propose a novel framework designed for true guidance-free FFP that resolves the critical tension between maintaining first-frame appearance and preserving source video motion. Architecturally, we introduce Adaptive Spatio-Temporal RoPE (AST-RoPE), which dynamically remaps positional encodings to disentangle appearance and motion references. At the objective level, we employ a self-distillation strategy where an identity propagation task acts as a powerful regularizer, ensuring long-term temporal stability and preventing semantic drift. Comprehensive experiments on the EditVerseBench benchmark demonstrate that our method significantly outperforming existing academic and commercial models by receiving about 0.2 PickScore and 0.3 VLM score improvement against these competitors. |
| title | FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.01720 |