Text-based Talking Video Editing with Cascaded Conditional Diffusion

• URL: http://arxiv.org/abs/2407.14841v1
• Date: Sat, 20 Jul 2024 10:55:19 GMT
• Title: Text-based Talking Video Editing with Cascaded Conditional Diffusion
• Authors: Bo Han, Heqing Zou, Haoyang Li, Guangcong Wang, Chng Eng Siong,
• Abstract summary: Text-based talking-head video editing aims to efficiently insert, delete, and substitute segments of talking videos. Previous works either require minutes of talking-face video training data and expensive test-time optimization for customized talking video editing. We propose an efficient cascaded conditional diffusion-based framework, which consists of two stages: audio to dense-landmark motion and motion to video.
• Score: 31.194060914767896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based talking-head video editing aims to efficiently insert, delete, and substitute segments of talking videos through a user-friendly text editing approach. It is challenging because of \textbf{1)} generalizable talking-face representation, \textbf{2)} seamless audio-visual transitions, and \textbf{3)} identity-preserved talking faces. Previous works either require minutes of talking-face video training data and expensive test-time optimization for customized talking video editing or directly generate a video sequence without considering in-context information, leading to a poor generalizable representation, or incoherent transitions, or even inconsistent identity. In this paper, we propose an efficient cascaded conditional diffusion-based framework, which consists of two stages: audio to dense-landmark motion and motion to video. \textit{\textbf{In the first stage}}, we first propose a dynamic weighted in-context diffusion module to synthesize dense-landmark motions given an edited audio. \textit{\textbf{In the second stage}}, we introduce a warping-guided conditional diffusion module. The module first interpolates between the start and end frames of the editing interval to generate smooth intermediate frames. Then, with the help of the audio-to-dense motion images, these intermediate frames are warped to obtain coarse intermediate frames. Conditioned on the warped intermedia frames, a diffusion model is adopted to generate detailed and high-resolution target frames, which guarantees coherent and identity-preserved transitions. The cascaded conditional diffusion model decomposes the complex talking editing task into two flexible generation tasks, which provides a generalizable talking-face representation, seamless audio-visual transitions, and identity-preserved faces on a small dataset. Experiments show the effectiveness and superiority of the proposed method.

Related papers

• TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation [4.019144083959918]
  We present TANGO, a framework for generating co-speech body-gesture videos. Given a few-minute, single-speaker reference video, TANGO produces high-fidelity videos with synchronized body gestures.
  arXiv  Detail & Related papers  (2024-10-05T16:30:46Z)
• COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing [57.76170824395532]
  Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video. We propose COrrespondence-guided Video Editing (COVE) to achieve high-quality and consistent video editing. COVE can be seamlessly integrated into the pre-trained T2I diffusion model without the need for extra training or optimization.
  arXiv  Detail & Related papers  (2024-06-13T06:27:13Z)
• OpFlowTalker: Realistic and Natural Talking Face Generation via Optical Flow Guidance [13.050998759819933]
  "OpFlowTalker" is a novel approach that utilizes predicted optical flow changes from audio inputs rather than direct image predictions. It smooths image transitions and aligns changes with semantic content. We also developed an optical flow synchronization module that regulates both full-face and lip movements.
  arXiv  Detail & Related papers  (2024-05-23T15:42:34Z)
• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• 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)
• 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)
• 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)
• Text-driven Video Prediction [83.04845684117835]
  We propose a new task called Text-driven Video Prediction (TVP) Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. To investigate the capability of text in causal inference for progressive motion information, our TVP framework contains a Text Inference Module (TIM)
  arXiv  Detail & Related papers  (2022-10-06T12:43:07Z)

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.