StereoDiffusion: Training-Free Stereo Image Generation Using Latent Diffusion Models

• URL: http://arxiv.org/abs/2403.04965v2
• Date: Sun, 2 Jun 2024 14:31:09 GMT
• Title: StereoDiffusion: Training-Free Stereo Image Generation Using Latent Diffusion Models
• Authors: Lezhong Wang, Jeppe Revall Frisvad, Mark Bo Jensen, Siavash Arjomand Bigdeli,
• Abstract summary: We introduce StereoDiffusion, a method that is trainning free, remarkably straightforward to use, and seamlessly integrates into the original Stable Diffusion model. Our method modifies the latent variable to provide an end-to-end, lightweight capability for fast generation of stereo image pairs. Our proposed method maintains a high standard of image quality throughout the stereo generation process, achieving state-of-the-art scores in various quantitative evaluations.
• Score: 2.9260206957981167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The demand for stereo images increases as manufacturers launch more XR devices. To meet this demand, we introduce StereoDiffusion, a method that, unlike traditional inpainting pipelines, is trainning free, remarkably straightforward to use, and it seamlessly integrates into the original Stable Diffusion model. Our method modifies the latent variable to provide an end-to-end, lightweight capability for fast generation of stereo image pairs, without the need for fine-tuning model weights or any post-processing of images. Using the original input to generate a left image and estimate a disparity map for it, we generate the latent vector for the right image through Stereo Pixel Shift operations, complemented by Symmetric Pixel Shift Masking Denoise and Self-Attention Layers Modification methods to align the right-side image with the left-side image. Moreover, our proposed method maintains a high standard of image quality throughout the stereo generation process, achieving state-of-the-art scores in various quantitative evaluations.

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