[2512.11438] Flowception: Temporally Expansive Flow Matching for Video Generation

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.11438 (cs) [Submitted on 12 Dec 2025 (v1), last revised 22 Mar 2026 (this version, v2)] Title:Flowception: Temporally Expansive Flow Matching for Video Generation Authors:Tariq Berrada Ifriqi, John Nguyen, Karteek Alahari, Jakob Verbeek, Ricky T. Q. Chen View a PDF of the paper titled Flowception: Temporally Expansive Flow Matching for Video Generation, by Tariq Berrada Ifriqi and 4 other authors View PDF HTML (experimental) Abstract: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. Subjects...