title: X-Dyna: Expressive Dynamic Human Image Animation
summary: We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for
  animating a single human image using facial expressions and body movements
  derived from a driving video, that generates realistic, context-aware dynamics
  for both the subject and the surrounding environment. Building on prior
  approaches centered on human pose control, X-Dyna addresses key shortcomings
  causing the loss of dynamic details, enhancing the lifelike qualities of human
  video animations. At the core of our approach is the Dynamics-Adapter, a
  lightweight module that effectively integrates reference appearance context
  into the spatial attentions of the diffusion backbone while preserving the
  capacity of motion modules in synthesizing fluid and intricate dynamic details.
  Beyond body pose control, we connect a local control module with our model to
  capture identity-disentangled facial expressions, facilitating accurate
  expression transfer for enhanced realism in animated scenes. Together, these
  components form a unified framework capable of learning physical human motion
  and natural scene dynamics from a diverse blend of human and scene videos.
  Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna
  outperforms state-of-the-art methods, creating highly lifelike and expressive
  animations. The code is available at https://github.com/bytedance/X-Dyna.
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paper_authors.name: Di Chang
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paper_authors.user: primecai
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paper_authors.name: Chenxu Zhang
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paper_authors.name: Guoxian Song
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paper_authors.name: Chao Wang
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paper_authors.name: Yichun Shi
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paper_authors.name: Zeyuan Chen
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paper_authors.name: Gordon Wetzstein
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paper_authors.name: Mohammad Soleymani
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paper_title: X-Dyna: Expressive Dynamic Human Image Animation
paper_summary: We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for
  animating a single human image using facial expressions and body movements
  derived from a driving video, that generates realistic, context-aware dynamics
  for both the subject and the surrounding environment. Building on prior
  approaches centered on human pose control, X-Dyna addresses key shortcomings
  causing the loss of dynamic details, enhancing the lifelike qualities of human
  video animations. At the core of our approach is the Dynamics-Adapter, a
  lightweight module that effectively integrates reference appearance context
  into the spatial attentions of the diffusion backbone while preserving the
  capacity of motion modules in synthesizing fluid and intricate dynamic details.
  Beyond body pose control, we connect a local control module with our model to
  capture identity-disentangled facial expressions, facilitating accurate
  expression transfer for enhanced realism in animated scenes. Together, these
  components form a unified framework capable of learning physical human motion
  and natural scene dynamics from a diverse blend of human and scene videos.
  Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna
  outperforms state-of-the-art methods, creating highly lifelike and expressive
  animations. The code is available at https://github.com/bytedance/X-Dyna.
paper_ai_summary: X-Dyna uses a diffusion-based pipeline and Dynamics-Adapter to animate human images by integrating facial expressions and body movements, resulting in realistic and expressive videos.
paper_ai_keywords: zero-shot diffusion-based pipeline facial expressions body movements dynamic details lifelike qualities Dynamics-Adapter spatial attentions diffusion backbone motion modules fluid and intricate dynamic details identity-disentangled facial expressions expression transfer natural scene dynamics physical human motion