Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait

• URL: http://arxiv.org/abs/2503.12963v1
• Date: Mon, 17 Mar 2025 09:18:31 GMT
• Title: Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait
• Authors: Chaolong Yang, Kai Yao, Yuyao Yan, Chenru Jiang, Weiguang Zhao, Jie Sun, Guangliang Cheng, Yifei Zhang, Bin Dong, Kaizhu Huang,
• Abstract summary: We propose KDTalker, the first framework to combine unsupervised implicit 3D keypoint with atemporal diffusion model.<n> KDTalker adapts facial information densities, allowing the diffusion process to model diverse head poses and capture fine facial details flexibly.
• Score: 30.481914742000697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Audio-driven single-image talking portrait generation plays a crucial role in virtual reality, digital human creation, and filmmaking. Existing approaches are generally categorized into keypoint-based and image-based methods. Keypoint-based methods effectively preserve character identity but struggle to capture fine facial details due to the fixed points limitation of the 3D Morphable Model. Moreover, traditional generative networks face challenges in establishing causality between audio and keypoints on limited datasets, resulting in low pose diversity. In contrast, image-based approaches produce high-quality portraits with diverse details using the diffusion network but incur identity distortion and expensive computational costs. In this work, we propose KDTalker, the first framework to combine unsupervised implicit 3D keypoint with a spatiotemporal diffusion model. Leveraging unsupervised implicit 3D keypoints, KDTalker adapts facial information densities, allowing the diffusion process to model diverse head poses and capture fine facial details flexibly. The custom-designed spatiotemporal attention mechanism ensures accurate lip synchronization, producing temporally consistent, high-quality animations while enhancing computational efficiency. Experimental results demonstrate that KDTalker achieves state-of-the-art performance regarding lip synchronization accuracy, head pose diversity, and execution efficiency.Our codes are available at https://github.com/chaolongy/KDTalker.

Related papers

• 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)
• Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models [69.50286698375386]
  We propose a novel approach that better harnesses diffusion models for face-swapping. We introduce a mask shuffling technique during inpainting training, which allows us to create a so-called universal model for swapping. Ours is a relatively unified approach and so it is resilient to errors in other off-the-shelf models.
  arXiv  Detail & Related papers  (2024-09-11T13:43:53Z)
• KMTalk: Speech-Driven 3D Facial Animation with Key Motion Embedding [19.15471840100407]
  We present a novel approach for synthesizing 3D facial motions from audio sequences using key motion embeddings. Our method integrates linguistic and data-driven priors through two modules: the linguistic-based key motion acquisition and the cross-modal motion completion. The latter extends key motions into a full sequence of 3D talking faces guided by audio features, improving temporal coherence and audio-visual consistency.
  arXiv  Detail & Related papers  (2024-09-02T09:41:24Z)
• High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model [89.29655924125461]
  We propose a novel landmark-based diffusion model for talking face generation. We first establish the less ambiguous mapping from audio to landmark motion of lip and jaw. Then, we introduce an innovative conditioning module called TalkFormer to align the synthesized motion with the motion represented by landmarks.
  arXiv  Detail & Related papers  (2024-08-10T02:58:28Z)
• 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)
• NeRFFaceSpeech: One-shot Audio-driven 3D Talking Head Synthesis via Generative Prior [5.819784482811377]
  We propose a novel method, NeRFFaceSpeech, which enables to produce high-quality 3D-aware talking head. Our method can craft a 3D-consistent facial feature space corresponding to a single image. We also introduce LipaintNet that can replenish the lacking information in the inner-mouth area.
  arXiv  Detail & Related papers  (2024-05-09T13:14:06Z)
• FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio [45.71036380866305]
  We abstract the process of people hearing speech, extracting meaningful cues, and creating dynamically audio-consistent talking faces from a single audio. Specifically, it involves two critical challenges: one is to effectively decouple identity, content, and emotion from entangled audio, and the other is to maintain intra-video diversity and inter-video consistency. We introduce the Controllable Coherent Frame generation, which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models.
  arXiv  Detail & Related papers  (2024-03-04T09:59:48Z)
• SAiD: Speech-driven Blendshape Facial Animation with Diffusion [6.4271091365094515]
  Speech-driven 3D facial animation is challenging due to the scarcity of large-scale visual-audio datasets. We propose a speech-driven 3D facial animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual to enhance lip synchronization.
  arXiv  Detail & Related papers  (2023-12-25T04:40:32Z)
• DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation [10.73030153404956]
  We propose a cross-modal dual-learning framework, termed DualTalker, to improve data usage efficiency. The framework is trained jointly with the primary task (audio-driven facial animation) and its dual task (lip reading) and shares common audio/motion encoder components. Our approach outperforms current state-of-the-art methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2023-11-08T15:39:56Z)
• GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation [71.73912454164834]
  A modern talking face generation method is expected to achieve the goals of generalized audio-lip synchronization, good video quality, and high system efficiency. NeRF has become a popular technique in this field since it could achieve high-fidelity and 3D-consistent talking face generation with a few-minute-long training video. We propose GeneFace++ to handle these challenges by utilizing the rendering pitch contour as an auxiliary feature and introducing a temporal loss in the facial motion prediction process.
  arXiv  Detail & Related papers  (2023-05-01T12:24:09Z)
• 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.