title: Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based
    Spatiotemporal Diffusion for Audio-driven Talking Portrait
summary: 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.
paper_authors._id: 67da8735cae81b233ffd2927
paper_authors.name: Chaolong Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Chaolong Yang
paper_authors.type: user
paper_authors.user: ChaolongYang
paper_authors._id: 67da8735cae81b233ffd2928
paper_authors.name: Kai Yao
paper_authors.status: extracted_pending
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paper_authors.fullname: kai yao
paper_authors.type: user
paper_authors.user: KaiserYaoJM
paper_authors._id: 67da8735cae81b233ffd2929
paper_authors.name: Yuyao Yan
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paper_authors.avatarUrl: /avatars/3d45b5048d7a55a8a31a928081d95d8a.svg
paper_authors.fullname: Yuyao Yan
paper_authors.type: user
paper_authors.user: Ritayy
paper_authors._id: 67da8735cae81b233ffd292a
paper_authors.name: Chenru Jiang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/5d4c4156463a7d69d88e579bf14215d0.svg
paper_authors.fullname: chenru jiang
paper_authors.type: user
paper_authors.user: chenrujiang
paper_authors._id: 67da8735cae81b233ffd292b
paper_authors.name: Weiguang Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T11:05:14.366Z
paper_authors._id: 672094a12174ca42d583e7bc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/OYcCwVQwR0oDQcVsMr22R.png
paper_authors.fullname: Weiguang Zhao
paper_authors.type: user
paper_authors.user: weiguangzhao
paper_authors._id: 67da8735cae81b233ffd292c
paper_authors.name: Jie Sun
paper_authors._id: 67da8735cae81b233ffd292d
paper_authors.name: Guangliang Cheng
paper_authors._id: 67da8735cae81b233ffd292e
paper_authors.name: Yifei Zhang
paper_authors._id: 67da8735cae81b233ffd292f
paper_authors.name: Bin Dong
paper_authors._id: 67da8735cae81b233ffd2930
paper_authors.name: Kaizhu Huang
paper_title: Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based
    Spatiotemporal Diffusion for Audio-driven Talking Portrait
paper_summary: 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.
paper_ai_summary: KDTalker integrates unsupervised implicit 3D keypoints with a spatiotemporal diffusion model to generate high-quality, detail-rich talking portraits with efficient computation and accurate lip synchronization.
paper_ai_keywords: 3D Morphable Model diffusion network unsupervised implicit 3D keypoints spatiotemporal diffusion model spatiotemporal attention mechanism