FacEDiT: Unified Talking Face Editing and Generation via Facial Motion Infilling

• URL: http://arxiv.org/abs/2512.14056v1
• Date: Tue, 16 Dec 2025 03:49:52 GMT
• Title: FacEDiT: Unified Talking Face Editing and Generation via Facial Motion Infilling
• Authors: Kim Sung-Bin, Joohyun Chang, David Harwath, Tae-Hyun Oh,
• Abstract summary: We look at talking face editing and generation as subtasks of speech-conditional motion infilling.<n>FacEDiT learns to synthesize masked facial motions conditioned on surrounding motions and speech.<n>FacEDiT produces accurate, speech-aligned facial edits with strong identity preservation and smooth visual continuity.
• Score: 47.23685762507782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Talking face editing and face generation have often been studied as distinct problems. In this work, we propose viewing both not as separate tasks but as subtasks of a unifying formulation, speech-conditional facial motion infilling. We explore facial motion infilling as a self-supervised pretext task that also serves as a unifying formulation of dynamic talking face synthesis. To instantiate this idea, we propose FacEDiT, a speech-conditional Diffusion Transformer trained with flow matching. Inspired by masked autoencoders, FacEDiT learns to synthesize masked facial motions conditioned on surrounding motions and speech. This formulation enables both localized generation and edits, such as substitution, insertion, and deletion, while ensuring seamless transitions with unedited regions. In addition, biased attention and temporal smoothness constraints enhance boundary continuity and lip synchronization. To address the lack of a standard editing benchmark, we introduce FacEDiTBench, the first dataset for talking face editing, featuring diverse edit types and lengths, along with new evaluation metrics. Extensive experiments validate that talking face editing and generation emerge as subtasks of speech-conditional motion infilling; FacEDiT produces accurate, speech-aligned facial edits with strong identity preservation and smooth visual continuity while generalizing effectively to talking face generation.

Related papers

• Text2Lip: Progressive Lip-Synced Talking Face Generation from Text via Viseme-Guided Rendering [53.2204901422631]
  Text2Lip is a viseme-centric framework that constructs an interpretable phonetic-visual bridge.<n>We show that Text2Lip outperforms existing approaches in semantic fidelity, visual realism, and modality robustness.
  arXiv  Detail & Related papers  (2025-08-04T12:50:22Z)
• Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservation [54.52905471078152]
  We propose a mask-free talking face generation approach while maintaining the 2D-based face editing task.<n>We transform the input images to have closed mouths, using a two-step landmark-based approach trained in an unpaired manner.
  arXiv  Detail & Related papers  (2025-07-28T16:03:36Z)
• Shushing! Let's Imagine an Authentic Speech from the Silent Video [15.426152742881365]
  Vision-guided speech generation aims to produce authentic speech from facial appearance or lip motions without relying on auditory signals.<n>Despite recent progress, existing methods struggle to achieve unified cross-modal alignment across semantics, timbre, and emotional prosody from visual cues.<n>We introduce ImaginTalk, a novel cross-modal diffusion framework that generates faithful speech using only visual input.
  arXiv  Detail & Related papers  (2025-03-19T06:28:17Z)
• 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)
• RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network [48.95833484103569]
  RealTalk is an audio-to-expression transformer and a high-fidelity expression-to-face framework. In the first component, we consider both identity and intra-personal variation features related to speaking lip movements. In the second component, we design a lightweight facial identity alignment (FIA) module. This novel design allows us to generate fine details in real-time, without depending on sophisticated and inefficient feature alignment modules.
  arXiv  Detail & Related papers  (2024-06-26T12:09:59Z)
• Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation [12.044308738509402]
  We propose a two-stage audio-driven talking face generation framework that employs 3D facial landmarks as intermediate variables. This framework achieves collaborative alignment of expression, gaze, and pose with emotions through self-supervised learning. Our model significantly advances the state-of-the-art performance in both visual quality and emotional alignment.
  arXiv  Detail & Related papers  (2024-06-12T06:00:00Z)
• SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation [13.459396544300137]
  We propose a novel one-shot Talking Head Generation framework (SPEAK) that distinguishes itself from the general Talking Face Generation. We introduce Inter-Reconstructed Feature Disentanglement (IRFD) module to decouple facial features into three latent spaces. We then design a face editing module that modifies speech content and facial latent codes into a single latent space.
  arXiv  Detail & Related papers  (2024-05-12T11:41:44Z)
• 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)
• Imitator: Personalized Speech-driven 3D Facial Animation [63.57811510502906]
  State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor. We present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video. We show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.
  arXiv  Detail & Related papers  (2022-12-30T19:00:02Z)
• Continuously Controllable Facial Expression Editing in Talking Face Videos [34.83353695337335]
  Speech-related expressions and emotion-related expressions are often highly coupled. Traditional image-to-image translation methods cannot work well in our application. We propose a high-quality facial expression editing method for talking face videos.
  arXiv  Detail & Related papers  (2022-09-17T09:05:47Z)
• 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.