LOVECon: Text-driven Training-Free Long Video Editing with ControlNet

• URL: http://arxiv.org/abs/2310.09711v3
• Date: Tue, 28 May 2024 07:04:03 GMT
• Title: LOVECon: Text-driven Training-Free Long Video Editing with ControlNet
• Authors: Zhenyi Liao, Zhijie Deng,
• Abstract summary: This paper aims to bridge the gap, establishing a simple and effective baseline for training-free diffusion model-based long video editing. We build the pipeline upon ControlNet, which excels at various image editing tasks based on text prompts. Our method manages to edit videos comprising hundreds of frames according to user requirements.
• Score: 9.762680144118061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Leveraging pre-trained conditional diffusion models for video editing without further tuning has gained increasing attention due to its promise in film production, advertising, etc. Yet, seminal works in this line fall short in generation length, temporal coherence, or fidelity to the source video. This paper aims to bridge the gap, establishing a simple and effective baseline for training-free diffusion model-based long video editing. As suggested by prior arts, we build the pipeline upon ControlNet, which excels at various image editing tasks based on text prompts. To break down the length constraints caused by limited computational memory, we split the long video into consecutive windows and develop a novel cross-window attention mechanism to ensure the consistency of global style and maximize the smoothness among windows. To achieve more accurate control, we extract the information from the source video via DDIM inversion and integrate the outcomes into the latent states of the generations. We also incorporate a video frame interpolation model to mitigate the frame-level flickering issue. Extensive empirical studies verify the superior efficacy of our method over competing baselines across scenarios, including the replacement of the attributes of foreground objects, style transfer, and background replacement. Besides, our method manages to edit videos comprising hundreds of frames according to user requirements. Our project is open-sourced and the project page is at https://github.com/zhijie-group/LOVECon.

Related papers

• Neural Video Fields Editing [56.558490998753456]
  NVEdit is a text-driven video editing framework designed to mitigate memory overhead and improve consistency. We construct a neural video field, powered by tri-plane and sparse grid, to enable encoding long videos with hundreds of frames. Next, we update the video field through off-the-shelf Text-to-Image (T2I) models to text-driven editing effects.
  arXiv  Detail & Related papers  (2023-12-12T14:48:48Z)
• FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing [65.60744699017202]
  We introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing. Our method, FLATTEN, enforces the patches on the same flow path across different frames to attend to each other in the attention module. Results on existing text-to-video editing benchmarks show that our proposed method achieves the new state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-09T17:59:53Z)
• Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models [65.268245109828]
  Ground-A-Video is a video-to-video translation framework for multi-attribute video editing. It attains temporally consistent editing of input videos in a training-free manner. Experiments and applications demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline methods in terms of edit-accuracy and frame consistency.
  arXiv  Detail & Related papers  (2023-10-02T11:28:37Z)
• InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing [27.661609140918916]
  InFusion is a framework for zero-shot text-based video editing. It supports editing of multiple concepts with pixel-level control over diverse concepts mentioned in the editing prompt. Our framework is a low-cost alternative to one-shot tuned models for editing since it does not require training.
  arXiv  Detail & Related papers  (2023-07-22T17:05:47Z)
• TokenFlow: Consistent Diffusion Features for Consistent Video Editing [27.736354114287725]
  We present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method.
  arXiv  Detail & Related papers  (2023-07-19T18:00:03Z)
• VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing [18.24307442582304]
  We introduce VidEdit, a novel method for zero-shot text-based video editing. Our experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset.
  arXiv  Detail & Related papers  (2023-06-14T19:15:49Z)
• Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation [93.18163456287164]
  This paper proposes a novel text-guided video-to-video translation framework to adapt image models to videos. Our framework achieves global style and local texture temporal consistency at a low cost.
  arXiv  Detail & Related papers  (2023-06-13T17:52:23Z)
• ControlVideo: Training-free Controllable Text-to-Video Generation [117.06302461557044]
  ControlVideo is a framework to enable natural and efficient text-to-video generation. It generates both short and long videos within several minutes using one NVIDIA 2080Ti.
  arXiv  Detail & Related papers  (2023-05-22T14:48:53Z)
• FateZero: Fusing Attentions for Zero-shot Text-based Video Editing [104.27329655124299]
  We propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. Our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
  arXiv  Detail & Related papers  (2023-03-16T17:51:13Z)
• 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)

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.