FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
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.
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Xijie Huang
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2026-01-07T13:45:36.672Z
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Xijie Huang
ScarletAce
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ScarletAce
695c971f6aa73bc11f0914eb
Chengming Xu
admin_assigned
2026-01-07T13:45:29.742Z
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Chengming Xu
ChengmingX
user
ChengmingX
695c971f6aa73bc11f0914ec
Donghao Luo
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Xiaobin Hu
695c971f6aa73bc11f0914ee
Peng Tang
695c971f6aa73bc11f0914ef
Xu Peng
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Jiangning Zhang
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Chengjie Wang
admin_assigned
2026-01-07T13:46:12.508Z
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Chengjie Wang
chengjie-wang
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chengjie-wang
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Yanwei Fu
admin_assigned
2026-01-07T13:46:19.075Z
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YANWEI
yanweifuture
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yanweifuture
FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing
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.
A new large-scale video dataset and framework are presented that enable effective first-frame propagation without runtime guidance through adaptive spatio-temporal positional encoding and self-distillation techniques.
First-Frame Propagation video editing training datasets temporal priors FFP-300K Adaptive Spatio-Temporal RoPE AST-RoPE self-distillation identity propagation task PickScore VLM score EditVerseBench