arXiv:2606.28094v1 Announce Type: cross Abstract: Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete. With billions of parameters and tens of denoising steps, diffusion-based models achieve strong removal performance at the expense of substantial computational cost, limiting their use in interactive applications and on edge devices. To address these challenges, we present OSOR (One-Step Object Removal),
The continuous drive for more efficient and performant AI models, especially in resource-constrained environments, is pushing for innovations like one-step diffusion methods.
This development addresses key limitations of current diffusion models, making advanced computational photography and content creation more accessible and faster, potentially accelerating adoption in interactive applications and edge devices.
The ability to perform high-quality object removal in a single step significantly reduces the computational overhead and latency associated with diffusion models, broadening their practical application.
- · Interactive AI applications
- · Edge device manufacturers
- · Content creation platforms
- · Computer vision developers
- · Proprietary multi-step diffusion model providers
- · CPU-bound image editing software
One-step diffusion models enable faster and more efficient object removal, making sophisticated image editing more accessible.
This efficiency gain could lead to a proliferation of AI-powered creative tools on mobile and embedded systems, democratizing advanced content generation.
The reduced computational cost might lower barriers to entry for AI model development and deployment, fostering further innovation and competition in the computer vision space.
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Read at arXiv cs.AI