FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation

• URL: http://arxiv.org/abs/2604.17625v1
• Date: Sun, 19 Apr 2026 21:38:21 GMT
• Title: FlowC2S: Flowing from Current to Succeeding Frames for Fast and Memory-Efficient Video Continuation
• Abstract summary: This paper introduces a novel methodology for generating fast and memory-efficient video continuations.<n>Our method, dubbed FlowC2S, fine-tunes a pre-trained text-to-video flow model to learn a vector field between the current and succeeding video chunks.
• Score: 4.429835955129622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel methodology for generating fast and memory-efficient video continuations. Our method, dubbed FlowC2S, fine-tunes a pre-trained text-to-video flow model to learn a vector field between the current and succeeding video chunks. Two design choices are key. First, we introduce inherent optimal couplings, utilizing temporally adjacent video chunks during training as a practical proxy for true optimal couplings, resulting in straighter flows. Second, we incorporate target inversion, injecting the inverted latent of the target chunk into the input representation to strengthen correspondences and improve visual fidelity. By flowing directly from current to succeeding frames, instead of the common combination of current frames with noise to generate a video continuation, we reduce the dimensionality of the model input by a factor of two. The proposed method, fine-tuned from LTXV and Wan, surpasses the state-of-the-art scores across quantitative evaluations with FID and FVD, with as few as five neural function evaluations.

Related papers

• EFlow: Fast Few-Step Video Generator Training from Scratch via Efficient Solution Flow [52.73538517981873]
  EFlow is an efficient few-step training framework for video diffusion transformers.<n> Gated Local-Global Attention is a token-droppable hybrid block.<n>Path-Drop Guided training replaces the expensive target with a computationally cheap, weak path.
  arXiv  Detail & Related papers  (2026-03-28T02:06:55Z)
• Flowception: Temporally Expansive Flow Matching for Video Generation [35.14803469800522]
  Flowception is a non-autoregressive and variable-length video generation framework.<n>It learns a probability path that interleaves discrete frame insertions with continuous frame denoising.<n>By learning to insert and denoise frames in a sequence, Flowception seamlessly integrates different tasks such as image-to-video generation and video.
  arXiv  Detail & Related papers  (2025-12-12T10:23:47Z)
• Deeply Supervised Flow-Based Generative Models [17.145600544446673]
  DeepFlow is a novel framework that enhances velocity representation through inter layer communication.<n>Powered by the improved deep supervision via the internal velocity alignment, DeepFlow converges 8 times faster on ImageNet with equivalent performance.<n>DeepFlow also outperforms baselines in text to image generation tasks, as evidenced by evaluations on MSCOCO and zero shot GenEval.
  arXiv  Detail & Related papers  (2025-03-18T17:58:08Z)
• Generative Video Bi-flow [14.053608981988793]
  We propose a novel generative video model to robustly learn temporal change as a neural Ordinary Differential Equation (ODE) flow.<n>We demonstrate unconditional video generation in a streaming manner for various video datasets.
  arXiv  Detail & Related papers  (2025-03-09T00:03:59Z)
• From Slow Bidirectional to Fast Autoregressive Video Diffusion Models [48.35054927704544]
  Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies.<n>We address this limitation by adapting a pretrained bidirectional diffusion transformer to an autoregressive transformer that generates frames on-the-fly.<n>Our model achieves a total score of 84.27 on the VBench-Long benchmark, surpassing all previous video generation models.
  arXiv  Detail & Related papers  (2024-12-10T18:59:50Z)
• ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler [53.98558445900626]
  Current image-to-video diffusion models, while powerful in generating videos from a single frame, need adaptation for two-frame conditioned generation.<n>We introduce a novel, bidirectional sampling strategy to address these off-manifold issues without requiring extensive re-noising or fine-tuning.<n>Our method employs sequential sampling along both forward and backward paths, conditioned on the start and end frames, respectively, ensuring more coherent and on-manifold generation of intermediate frames.
  arXiv  Detail & Related papers  (2024-10-08T03:01:54Z)
• Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models [64.2445487645478]
  Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio. We present Live2Diff, the first attempt at designing a video diffusion model with uni-directional temporal attention, specifically targeting live streaming video translation.
  arXiv  Detail & Related papers  (2024-07-11T17:34:51Z)
• Improving the Training of Rectified Flows [14.652876697052156]
  Diffusion models have shown great promise for image and video generation, but sampling from state-of-the-art models requires expensive numerical integration of a generative ODE. One approach for tackling this problem is rectified flows, which iteratively learn smooth ODE paths that are less susceptible to truncation error. We propose improved techniques for training rectified flows, allowing them to compete with emphknowledge distillation methods even in the low NFE setting. Our improved rectified flow outperforms the state-of-the-art distillation methods such as consistency distillation and progressive distillation in both one-step and two
  arXiv  Detail & Related papers  (2024-05-30T17:56:04Z)
• MemFlow: Optical Flow Estimation and Prediction with Memory [54.22820729477756]
  We present MemFlow, a real-time method for optical flow estimation and prediction with memory. Our method enables memory read-out and update modules for aggregating historical motion information in real-time. Our approach seamlessly extends to the future prediction of optical flow based on past observations.
  arXiv  Detail & Related papers  (2024-04-07T04:56:58Z)
• StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences [31.210626775505407]
  Occlusions between consecutive frames have long posed a significant challenge in optical flow estimation. We present a Streamlined In-batch Multi-frame (SIM) pipeline tailored to video input, attaining a similar level of time efficiency to two-frame networks. StreamFlow not only excels in terms of performance on challenging KITTI and Sintel datasets, with particular improvement in occluded areas.
  arXiv  Detail & Related papers  (2023-11-28T07:53:51Z)
• ReBotNet: Fast Real-time Video Enhancement [59.08038313427057]
  Most restoration networks are slow, have high computational bottleneck, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework to perform real-time enhancement for practical use-cases like live video calls and video streams. To evaluate our method, we emulate two new datasets that real-world video call and streaming scenarios, and show extensive results on multiple datasets where ReBotNet outperforms existing approaches with lower computations, reduced memory requirements, and faster inference time.
  arXiv  Detail & Related papers  (2023-03-23T17:58:05Z)
• Efficient Video Prediction via Sparsely Conditioned Flow Matching [24.32740918613266]
  We introduce a novel generative model for video prediction based on latent flow matching. We call our model Random frame conditioned flow Integration for VidEo pRediction, or, in short, RIVER.
  arXiv  Detail & Related papers  (2022-11-26T14:18:50Z)
• FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation [97.99012124785177]
  FLAVR is a flexible and efficient architecture that uses 3D space-time convolutions to enable end-to-end learning and inference for video framesupervised. We demonstrate that FLAVR can serve as a useful self- pretext task for action recognition, optical flow estimation, and motion magnification.
  arXiv  Detail & Related papers  (2020-12-15T18:59:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.