Temporally Consistent Semantic Video Editing

• URL: http://arxiv.org/abs/2206.10590v1
• Date: Tue, 21 Jun 2022 17:59:59 GMT
• Title: Temporally Consistent Semantic Video Editing
• Authors: Yiran Xu, Badour AlBahar, Jia-Bin Huang
• Abstract summary: We present a simple yet effective method to facilitate temporally coherent video editing. Our core idea is to minimize the temporal photometric inconsistency by optimizing both the latent code and the pre-trained generator.
• Score: 44.50322018842475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative adversarial networks (GANs) have demonstrated impressive image generation quality and semantic editing capability of real images, e.g., changing object classes, modifying attributes, or transferring styles. However, applying these GAN-based editing to a video independently for each frame inevitably results in temporal flickering artifacts. We present a simple yet effective method to facilitate temporally coherent video editing. Our core idea is to minimize the temporal photometric inconsistency by optimizing both the latent code and the pre-trained generator. We evaluate the quality of our editing on different domains and GAN inversion techniques and show favorable results against the baselines.

Related papers

• I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models [18.36472998650704]
  We introduce a novel and generic solution that extends the applicability of image editing tools to videos by propagating edits from a single frame to the entire video using a pre-trained image-to-video model. Our method, dubbed I2VEdit, adaptively preserves the visual and motion integrity of the source video depending on the extent of the edits.
  arXiv  Detail & Related papers  (2024-05-26T11:47:40Z)
• FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing [10.011515580084243]
  Existing approaches relying on image generation models for video editing suffer from time-consuming one-shot fine-tuning, additional condition extraction, or DDIM inversion. We propose FastVideoEdit, an efficient zero-shot video editing approach inspired by Consistency Models (CMs) Our method enables direct mapping from source video to target video with strong preservation ability utilizing a special variance schedule.
  arXiv  Detail & Related papers  (2024-03-10T17:12:01Z)
• DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing [66.43179841884098]
  Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. We propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing. Our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks.
  arXiv  Detail & Related papers  (2024-02-04T18:50:29Z)
• RIGID: Recurrent GAN Inversion and Editing of Real Face Videos [73.97520691413006]
  GAN inversion is indispensable for applying the powerful editability of GAN to real images. Existing methods invert video frames individually often leading to undesired inconsistent results over time. We propose a unified recurrent framework, named textbfRecurrent vtextbfIdeo textbfGAN textbfInversion and etextbfDiting (RIGID) Our framework learns the inherent coherence between input frames in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-08-11T12:17:24Z)
• 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)
• 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)
• Edit-A-Video: Single Video Editing with Object-Aware Consistency [49.43316939996227]
  We propose a video editing framework given only a pretrained TTI model and a single text, video> pair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules tuning and on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.
  arXiv  Detail & Related papers  (2023-03-14T14:35:59Z)
• EditGAN: High-Precision Semantic Image Editing [120.49401527771067]
  EditGAN is a novel method for high quality, high precision semantic image editing. We show that EditGAN can manipulate images with an unprecedented level of detail and freedom. We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data.
  arXiv  Detail & Related papers  (2021-11-04T22:36:33Z)
• Task-agnostic Temporally Consistent Facial Video Editing [84.62351915301795]
  We propose a task-agnostic, temporally consistent facial video editing framework. Based on a 3D reconstruction model, our framework is designed to handle several editing tasks in a more unified and disentangled manner. Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
  arXiv  Detail & Related papers  (2020-07-03T02:49:20Z)

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.