Flowception: Temporally Expansive Flow Matching for Video Generation
We present Flowception, a novel non-autoregressive and variable-length video generation framework. Flowception learns a probability path that interleaves discrete frame insertions with continuous frame denoising. Compared to autoregressive methods, Flowception alleviates error accumulation/drift as the frame insertion mechanism during sampling serves as an efficient compression mechanism to handle long-term context. Compared to full-sequence flows, our method reduces FLOPs for training three-fold, while also being more amenable to local attention variants, and allowing to learn the length of videos jointly with their content. Quantitative experimental results show improved FVD and VBench metrics over autoregressive and full-sequence baselines, which is further validated with qualitative results. Finally, by learning to insert and denoise frames in a sequence, Flowception seamlessly integrates different tasks such as image-to-video generation and video interpolation.
694070f4b21c43ffbf4b7658
Tariq Berrada Ifriqi
claimed_verified
2025-12-17T14:04:41.357Z
65858134e07563d421117279
/avatars/08fb3f98cd96aa1828f5b9929b30cbb7.svg
Tariq Berrada
TariqBerrada
user
TariqBerrada
694070f4b21c43ffbf4b7659
John Nguyen
claimed_verified
2025-12-16T15:09:20.244Z
648a14e002c8497f58ebff62
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John Nguyen
ngjhn
user
ngjhn
694070f4b21c43ffbf4b765a
Karteek Alahari
694070f4b21c43ffbf4b765b
Jakob Verbeek
694070f4b21c43ffbf4b765c
Ricky T. Q. Chen
Flowception: Temporally Expansive Flow Matching for Video Generation
We present Flowception, a novel non-autoregressive and variable-length video generation framework. Flowception learns a probability path that interleaves discrete frame insertions with continuous frame denoising. Compared to autoregressive methods, Flowception alleviates error accumulation/drift as the frame insertion mechanism during sampling serves as an efficient compression mechanism to handle long-term context. Compared to full-sequence flows, our method reduces FLOPs for training three-fold, while also being more amenable to local attention variants, and allowing to learn the length of videos jointly with their content. Quantitative experimental results show improved FVD and VBench metrics over autoregressive and full-sequence baselines, which is further validated with qualitative results. Finally, by learning to insert and denoise frames in a sequence, Flowception seamlessly integrates different tasks such as image-to-video generation and video interpolation.
Flowception enables efficient video generation through a hybrid approach that combines discrete frame insertion with continuous denoising, reducing computational costs while improving long-term context handling.
non-autoregressive variable-length video generation probability path discrete frame insertion continuous frame denoising error accumulation frame insertion mechanism FLOPs local attention variants video interpolation image-to-video generation