arXiv:2505.06668v2 Announce Type: replace-cross Abstract: We present StableMotion, a novel framework that leverages geometric and content priors from pretrained large-scale image diffusion models for motion estimation in single-image rectification tasks such as Stitched Image Rectangling (SIR) and Rolling Shutter Correction (RSC). Specifically, StableMotion takes a text-to-image Stable Diffusion (SD) model as its backbone and repurposes it as an image-to-motion estimator. To mitigate inconsistent outputs produced by diffusion models, we propose Adaptive Ensemble Strategy (AES), which consolida
The rapid advancement and widespread availability of large-scale image diffusion models like Stable Diffusion are enabling new applications in computer vision, moving beyond traditional image generation.
This development indicates a growing capability for AI to interpret and manipulate visual data for complex tasks like motion estimation, which is critical for robotics, autonomous systems, and media processing.
Diffusion models are now being repurposed as image-to-motion estimators, expanding their utility beyond content generation to analytical and rectification tasks, potentially improving automation and visual data precision.
- · AI research and development (R&D) institutions
- · Robotics and autonomous vehicle manufacturers
- · Computer vision companies
- · Content creation and post-production industry
- · Traditional motion estimation algorithm developers not leveraging diffusion mode
- · Industries reliant on manual visual data correction
Diffusion models gain new functional capabilities in motion estimation for rectification tasks.
Improved motion estimation leads to more robust autonomous systems and higher quality visual content.
The integration of advanced AI perception redefines operational capabilities across various industries, from manufacturing to entertainment, and could accelerate the development of agentic systems.
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