title: GenStereo: Towards Open-World Generation of Stereo Images and
    Unsupervised Matching
summary: Stereo images are fundamental to numerous applications, including extended
  reality (XR) devices, autonomous driving, and robotics. Unfortunately,
  acquiring high-quality stereo images remains challenging due to the precise
  calibration requirements of dual-camera setups and the complexity of obtaining
  accurate, dense disparity maps. Existing stereo image generation methods
  typically focus on either visual quality for viewing or geometric accuracy for
  matching, but not both. We introduce GenStereo, a diffusion-based approach, to
  bridge this gap. The method includes two primary innovations (1) conditioning
  the diffusion process on a disparity-aware coordinate embedding and a warped
  input image, allowing for more precise stereo alignment than previous methods,
  and (2) an adaptive fusion mechanism that intelligently combines the
  diffusion-generated image with a warped image, improving both realism and
  disparity consistency. Through extensive training on 11 diverse stereo
  datasets, GenStereo demonstrates strong generalization ability. GenStereo
  achieves state-of-the-art performance in both stereo image generation and
  unsupervised stereo matching tasks. Our framework eliminates the need for
  complex hardware setups while enabling high-quality stereo image generation,
  making it valuable for both real-world applications and unsupervised learning
  scenarios. Project page is available at https://qjizhi.github.io/genstereo
paper_authors._id: 67d985207fadfdd9d2a8fb93
paper_authors.name: Feng Qiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T14:57:40.079Z
paper_authors._id: 6487e839341b4c7ec11013e5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6487e839341b4c7ec11013e5/QxF1MmaGm67ZREYs1TYvn.jpeg
paper_authors.fullname: Feng Qiao
paper_authors.type: user
paper_authors.user: FQiao
paper_authors._id: 67d985207fadfdd9d2a8fb94
paper_authors.name: Zhexiao Xiong
paper_authors._id: 67d985207fadfdd9d2a8fb95
paper_authors.name: Eric Xing
paper_authors._id: 67d985207fadfdd9d2a8fb96
paper_authors.name: Nathan Jacobs
paper_title: GenStereo: Towards Open-World Generation of Stereo Images and
    Unsupervised Matching
paper_summary: Stereo images are fundamental to numerous applications, including extended
  reality (XR) devices, autonomous driving, and robotics. Unfortunately,
  acquiring high-quality stereo images remains challenging due to the precise
  calibration requirements of dual-camera setups and the complexity of obtaining
  accurate, dense disparity maps. Existing stereo image generation methods
  typically focus on either visual quality for viewing or geometric accuracy for
  matching, but not both. We introduce GenStereo, a diffusion-based approach, to
  bridge this gap. The method includes two primary innovations (1) conditioning
  the diffusion process on a disparity-aware coordinate embedding and a warped
  input image, allowing for more precise stereo alignment than previous methods,
  and (2) an adaptive fusion mechanism that intelligently combines the
  diffusion-generated image with a warped image, improving both realism and
  disparity consistency. Through extensive training on 11 diverse stereo
  datasets, GenStereo demonstrates strong generalization ability. GenStereo
  achieves state-of-the-art performance in both stereo image generation and
  unsupervised stereo matching tasks. Our framework eliminates the need for
  complex hardware setups while enabling high-quality stereo image generation,
  making it valuable for both real-world applications and unsupervised learning
  scenarios. Project page is available at https://qjizhi.github.io/genstereo
paper_ai_summary: GenStereo, a diffusion-based approach, generates high-quality stereo images with precise alignment and disparity consistency by conditioning on a disparity-aware coordinate embedding and using an adaptive fusion mechanism.
paper_ai_keywords: diffusion-based approach disparity-aware coordinate embedding warped input image adaptive fusion mechanism stereo image generation unsupervised stereo matching