SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing

• URL: http://arxiv.org/abs/2409.03605v1
• Date: Thu, 5 Sep 2024 15:11:40 GMT
• Title: SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing
• Authors: Lingyu Xiong, Xize Cheng, Jintao Tan, Xianjia Wu, Xiandong Li, Lei Zhu, Fei Ma, Minglei Li, Huang Xu, Zhihu Hu,
• Abstract summary: We propose a novel framework called SegTalker to decouple lip movements and image textures. We disentangle semantic regions of image into style codes using a mask-guided encoder. Ultimately, we inject the previously generated talking segmentation and style codes into a mask-guided StyleGAN to synthesize video frame.
• Score: 19.245228801339007
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Audio-driven talking face generation aims to synthesize video with lip movements synchronized to input audio. However, current generative techniques face challenges in preserving intricate regional textures (skin, teeth). To address the aforementioned challenges, we propose a novel framework called SegTalker to decouple lip movements and image textures by introducing segmentation as intermediate representation. Specifically, given the mask of image employed by a parsing network, we first leverage the speech to drive the mask and generate talking segmentation. Then we disentangle semantic regions of image into style codes using a mask-guided encoder. Ultimately, we inject the previously generated talking segmentation and style codes into a mask-guided StyleGAN to synthesize video frame. In this way, most of textures are fully preserved. Moreover, our approach can inherently achieve background separation and facilitate mask-guided facial local editing. In particular, by editing the mask and swapping the region textures from a given reference image (e.g. hair, lip, eyebrows), our approach enables facial editing seamlessly when generating talking face video. Experiments demonstrate that our proposed approach can effectively preserve texture details and generate temporally consistent video while remaining competitive in lip synchronization. Quantitative and qualitative results on the HDTF and MEAD datasets illustrate the superior performance of our method over existing methods.

Related papers

• JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation [24.2065254076207]
  We introduce a novel method for joint expression and audio-guided talking face generation. Our method can synthesize high-fidelity talking face videos, achieving state-of-the-art facial expression transfer.
  arXiv  Detail & Related papers  (2024-09-18T17:18:13Z)
• Text-Guided Video Masked Autoencoder [12.321239366215426]
  We introduce a novel text-guided masking algorithm (TGM) that masks the video regions with highest correspondence to paired captions. We show that across existing masking algorithms, unifying MAE and masked video-text contrastive learning improves downstream performance compared to pure MAE.
  arXiv  Detail & Related papers  (2024-08-01T17:58:19Z)
• Lazy Diffusion Transformer for Interactive Image Editing [79.75128130739598]
  We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, starting from a blank canvas or an image, a user specifies a sequence of localized image modifications.
  arXiv  Detail & Related papers  (2024-04-18T17:59:27Z)
• GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance [83.43852715997596]
  GSmoothFace is a novel two-stage generalized talking face generation model guided by a fine-grained 3d face model. It can synthesize smooth lip dynamics while preserving the speaker's identity. Both quantitative and qualitative experiments confirm the superiority of our method in terms of realism, lip synchronization, and visual quality.
  arXiv  Detail & Related papers  (2023-12-12T16:00:55Z)
• Identity-Preserving Talking Face Generation with Landmark and Appearance Priors [106.79923577700345]
  Existing person-generic methods have difficulty in generating realistic and lip-synced videos. We propose a two-stage framework consisting of audio-to-landmark generation and landmark-to-video rendering procedures. Our method can produce more realistic, lip-synced, and identity-preserving videos than existing person-generic talking face generation methods.
  arXiv  Detail & Related papers  (2023-05-15T01:31:32Z)
• Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval [19.61947785487129]
  Mask for Semantics Completion (MASCOT) based on semantic-based masked modeling. Our MASCOT performs state-of-the-art performance on four major text-video retrieval benchmarks.
  arXiv  Detail & Related papers  (2023-05-13T12:31:37Z)
• Masked Lip-Sync Prediction by Audio-Visual Contextual Exploitation in Transformers [91.00397473678088]
  Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. We propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality. Our model can generate high-fidelity lip-synced results for arbitrary subjects.
  arXiv  Detail & Related papers  (2022-12-09T16:32:46Z)
• Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling [61.03262873980619]
  Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. We propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images. Our framework is capable of labeling novel classes in captions via their word semantics to self-train a student model.
  arXiv  Detail & Related papers  (2021-11-24T18:50:47Z)
• Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation [96.66010515343106]
  We propose a clean yet effective framework to generate pose-controllable talking faces. We operate on raw face images, using only a single photo as an identity reference. Our model has multiple advanced capabilities including extreme view robustness and talking face frontalization.
  arXiv  Detail & Related papers  (2021-04-22T15:10:26Z)

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.