Frame-Level Captions for Long Video Generation with Complex Multi Scenes

• URL: http://arxiv.org/abs/2505.20827v1
• Date: Tue, 27 May 2025 07:39:43 GMT
• Title: Frame-Level Captions for Long Video Generation with Complex Multi Scenes
• Authors: Guangcong Zheng, Jianlong Yuan, Bo Wang, Haoyang Huang, Guoqing Ma, Nan Duan,
• Abstract summary: We propose a novel way to annotate datasets at the frame-level.<n>This detailed guidance works with a Frame-Level Attention Mechanism to make sure text and video match precisely.<n>Our training uses Diffusion Forcing to provide the model with the ability to handle time flexibly.
• Score: 52.12699618126831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating long videos that can show complex stories, like movie scenes from scripts, has great promise and offers much more than short clips. However, current methods that use autoregression with diffusion models often struggle because their step-by-step process naturally leads to a serious error accumulation (drift). Also, many existing ways to make long videos focus on single, continuous scenes, making them less useful for stories with many events and changes. This paper introduces a new approach to solve these problems. First, we propose a novel way to annotate datasets at the frame-level, providing detailed text guidance needed for making complex, multi-scene long videos. This detailed guidance works with a Frame-Level Attention Mechanism to make sure text and video match precisely. A key feature is that each part (frame) within these windows can be guided by its own distinct text prompt. Our training uses Diffusion Forcing to provide the model with the ability to handle time flexibly. We tested our approach on difficult VBench 2.0 benchmarks ("Complex Plots" and "Complex Landscapes") based on the WanX2.1-T2V-1.3B model. The results show our method is better at following instructions in complex, changing scenes and creates high-quality long videos. We plan to share our dataset annotation methods and trained models with the research community. Project page: https://zgctroy.github.io/frame-level-captions .

Related papers

• Enhancing Long Video Question Answering with Scene-Localized Frame Grouping [19.83545369186771]
  Current Multimodal Large Language Models (MLLMs) often perform poorly in long video understanding.<n>We propose a new scenario under the video question-answering task, SceneQA.<n>We introduce a novel method called SLFG to combine individual frames into semantically coherent scene frames.
  arXiv  Detail & Related papers  (2025-08-05T02:28:58Z)
• Video Diffusion Transformers are In-Context Learners [31.736838809714726]
  This paper investigates a solution for enabling in-context capabilities of video diffusion transformers.<n>We propose a simple pipeline to leverage in-context generation: ($textbfii$) videos along spacial or time dimension.<n>Our framework presents a valuable tool for the research community and offers critical insights for advancing product-level controllable video generation systems.
  arXiv  Detail & Related papers  (2024-12-14T10:39:55Z)
• Video Decomposition Prior: A Methodology to Decompose Videos into Layers [74.36790196133505]
  This paper introduces a novel video decomposition prior VDP' framework which derives inspiration from professional video editing practices.<n>VDP framework decomposes a video sequence into a set of multiple RGB layers and associated opacity levels.<n>We address tasks such as video object segmentation, dehazing, and relighting.
  arXiv  Detail & Related papers  (2024-12-06T10:35:45Z)
• MovieBench: A Hierarchical Movie Level Dataset for Long Video Generation [62.85764872989189]
  There is no publicly available dataset tailored for the analysis, evaluation, and training of long video generation models.<n>We present MovieBench: A Hierarchical Movie-Level dataset for Long Video Generation.<n>The dataset will be public and continuously maintained, aiming to advance the field of long video generation.
  arXiv  Detail & Related papers  (2024-11-22T10:25:08Z)
• Whats in a Video: Factorized Autoregressive Decoding for Online Dense Video Captioning [71.94122309290537]
  We propose an efficient, online approach to generate dense captions for videos. Our model uses a novel autoregressive factorized decoding architecture. Our approach shows excellent performance compared to both offline and online methods, and uses 20% less compute.
  arXiv  Detail & Related papers  (2024-11-22T02:46:44Z)
• VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning [62.51232333352754]
  VideoDirectorGPT is a novel framework for consistent multi-scene video generation. Our proposed framework substantially improves layout and movement control in both single- and multi-scene video generation.
  arXiv  Detail & Related papers  (2023-09-26T17:36:26Z)
• Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation [92.55296042611886]
  We propose a framework called "Reuse and Diffuse" dubbed $textitVidRD$ to produce more frames following the frames already generated by an LDM. We also propose a set of strategies for composing video-text data that involve diverse content from multiple existing datasets.
  arXiv  Detail & Related papers  (2023-09-07T08:12:58Z)
• Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators [70.17041424896507]
  Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. We propose a new task of zero-shot text-to-video generation using existing text-to-image synthesis methods. Our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.
  arXiv  Detail & Related papers  (2023-03-23T17:01:59Z)
• Multi-modal Video Chapter Generation [11.658507807110645]
  We introduce a new dataset called Chapter-Gen, which consists of approximately 10k user-generated videos with annotated chapter information. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Our experiments demonstrate that the proposed framework achieves superior results over existing methods.
  arXiv  Detail & Related papers  (2022-09-26T13:44:48Z)
• Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method [6.172652648945223]
  This paper presents a novel weakly-supervised methodology to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space.
  arXiv  Detail & Related papers  (2022-03-29T17:43:01Z)
• Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual Data [1.004766879203303]
  We present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video. We propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space.
  arXiv  Detail & Related papers  (2020-03-31T14:07:45Z)

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.