GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation

• URL: http://arxiv.org/abs/2506.21513v2
• Date: Thu, 10 Jul 2025 06:36:05 GMT
• Title: GGTalker: Talking Head Systhesis with Generalizable Gaussian Priors and Identity-Specific Adaptation
• Authors: Wentao Hu, Shunkai Li, Ziqiao Peng, Haoxian Zhang, Fan Shi, Xiaoqiang Liu, Pengfei Wan, Di Zhang, Hui Tian,
• Abstract summary: GGTalker synthesizes talking heads through a combination of generalizable priors and identity-specific adaptation.<n> GGTalker achieves state-of-the-art performance in rendering quality, 3D consistency, lip-sync accuracy, and training efficiency.
• Score: 20.17978153568009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating high-quality, generalizable speech-driven 3D talking heads remains a persistent challenge. Previous methods achieve satisfactory results for fixed viewpoints and small-scale audio variations, but they struggle with large head rotations and out-of-distribution (OOD) audio. Moreover, they are constrained by the need for time-consuming, identity-specific training. We believe the core issue lies in the lack of sufficient 3D priors, which limits the extrapolation capabilities of synthesized talking heads. To address this, we propose GGTalker, which synthesizes talking heads through a combination of generalizable priors and identity-specific adaptation. We introduce a two-stage Prior-Adaptation training strategy to learn Gaussian head priors and adapt to individual characteristics. We train Audio-Expression and Expression-Visual priors to capture the universal patterns of lip movements and the general distribution of head textures. During the Customized Adaptation, individual speaking styles and texture details are precisely modeled. Additionally, we introduce a color MLP to generate fine-grained, motion-aligned textures and a Body Inpainter to blend rendered results with the background, producing indistinguishable, photorealistic video frames. Comprehensive experiments show that GGTalker achieves state-of-the-art performance in rendering quality, 3D consistency, lip-sync accuracy, and training efficiency.

Related papers

• PTalker: Personalized Speech-Driven 3D Talking Head Animation via Style Disentanglement and Modality Alignment [19.347893658924367]
  "PTalker" is a novel framework for personalized 3D talking head animation.<n>It preserves speaking style through style disentanglement from audio and facial motion sequences.<n>It enhances lip-synchronization accuracy through a three-level alignment mechanism between audio and mesh modalities.
  arXiv  Detail & Related papers  (2025-12-27T14:14:14Z)
• D^3-Talker: Dual-Branch Decoupled Deformation Fields for Few-Shot 3D Talking Head Synthesis [28.923949756720425]
  A key challenge in 3D talking head synthesis lies in the reliance on a long-duration talking head video to train a new model from scratch.<n>Recent methods have attempted to address this issue by extracting general features from audio through pre-training models.<n>This paper proposes D3-Talker, a novel approach that constructs a static 3D Gaussian attribute field and employs audio and Facial Motion signals.
  arXiv  Detail & Related papers  (2025-08-20T06:12:33Z)
• GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expression [33.886734972316326]
  GoHD is a framework designed to produce highly realistic, expressive, and controllable portrait videos from any reference identity with any motion.<n>An animation module utilizing latent navigation is introduced to improve the generalization ability across unseen input styles.<n>A conformer-structured conditional diffusion model is designed to guarantee head poses that are aware of prosody.<n>A two-stage training strategy is devised to decouple frequent and frame-wise lip motion distillation from the generation of other more temporally dependent but less audio-related motions.
  arXiv  Detail & Related papers  (2024-12-12T14:12:07Z)
• GaussianSpeech: Audio-Driven Gaussian Avatars [76.10163891172192]
  We introduce GaussianSpeech, a novel approach that synthesizes high-fidelity animation sequences of photo-realistic, personalized 3D human head avatars from spoken audio.<n>We propose a compact and efficient 3DGS-based avatar representation that generates expression-dependent color and leverages wrinkle- and perceptually-based losses to synthesize facial details.
  arXiv  Detail & Related papers  (2024-11-27T18:54:08Z)
• MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes [74.82911268630463]
  Talking face generation (TFG) aims to animate a target identity's face to create realistic talking videos. MimicTalk exploits the rich knowledge from a NeRF-based person-agnostic generic model for improving the efficiency and robustness of personalized TFG. Experiments show that our MimicTalk surpasses previous baselines regarding video quality, efficiency, and expressiveness.
  arXiv  Detail & Related papers  (2024-10-09T10:12:37Z)
• 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)
• 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)
• DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering [69.9557427451339]
  We propose a framework based on neural radiance field to pursue high-fidelity talking head generation. Specifically, neural radiance field takes lip movements features and personalized attributes as two disentangled conditions. We show that our method achieves significantly better results than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-03T18:23:38Z)
• FaceFormer: Speech-Driven 3D Facial Animation with Transformers [46.8780140220063]
  Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. We propose a Transformer-based autoregressive model, FaceFormer, which encodes the long-term audio context and autoregressively predicts a sequence of animated 3D face meshes.
  arXiv  Detail & Related papers  (2021-12-10T04:21:59Z)

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.