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Abstract:

This paper introduces LookingGlass, a novel method for generating high-quality, ambiguous anamorphic illusions using latent rectified flow models. Anamorphoses are intentionally distorted images that appear unrecognizable when viewed directly, revealing a hidden image only when seen through specific optical devices like mirrors or lenses. Unlike previous methods limited to pixel-space diffusion models or orthogonal transformations, LookingGlass extends to latent space models and complex 3D projections, enabling intricate anamorphoses that also retain a valid interpretation when viewed normally. A core innovation is Laplacian Pyramid Warping (LPW), a frequency-aware image warping technique vital for preserving high-frequency details despite extreme distortions. The method incorporates VAE encoding/decoding and residual correction to prevent visual artifacts and enhance image quality. Quantitative and qualitative evaluations, including a user study, demonstrate LookingGlass's superiority in quality and expressiveness over prior approaches for various 2D transformations and complex catadioptric anamorphoses (cylindrical and conic mirrors, and Nicéron's lens).

Exploring LookingGlass: Generating Complex Anamorphoses with Latent Diffusion Models

• Introduction to Anamorphosis:
  • Anamorphosis, derived from Greek for "back" and "form," refers to images deliberately distorted to be unrecognizable when viewed directly.
  • Their true form is revealed only from a precise vantage point or through reflective/refractive surfaces, known as anamorphoscopes.
  • Historically, these illusions gained popularity in the 17th century but typically lose meaning when viewed normally.
• Proposed Method: LookingGlass:
  • This paper introduces a method for creating anamorphic images using latent text-to-image models, specifically focusing on setups where the image maintains a valid interpretation when viewed as-is.
  • It extends the "Visual Anagrams" framework to latent space models and supports a broader range of spatial transformations beyond simple orthogonal ones.
  • The goal is to improve the quality and expressiveness of generated illusions, especially for complex reflective and refractive surfaces.
• Addressing Prior Limitations:
  • Previous approaches often suffered from lower image quality, long inference times, or were limited to orthogonal transformations (e.g., flips, rotations) in pixel space.
  • LookingGlass enables artifact-free generation with latent diffusion and flow models, improving accessibility and quality for complex deformations.
• Key Component: Laplacian Pyramid Warping (LPW):
  • A novel, robust, frequency-aware image warping technique that handles complex image transformations while preserving high-frequency details, essential for intricate anamorphoses.
  • Forward Laplacian Warping: Computes the transformed image by sampling from a Gaussian pyramid of the input image, selecting appropriate levels of detail (LOD) based on the local compression or stretching of the view function, preventing aliasing.
  • Inverse Laplacian Warping: Transforms an image back into a Laplacian pyramid in the canonical view, effectively mapping pixels to their corresponding locations and frequency bands in the original space, robustly handling missing values from extreme distortions.
  • Laplacian Pyramid Blending: Aggregates different canonical views by averaging their respective Laplacian pyramid levels, weighted by masks for partial views, which helps preserve details better than standard averaging.
• Latent Visual Anagrams Enhancements:
  • VAE Encoding/Decoding: Image transformations are performed in pixel space (by decoding the estimated clean latent image, transforming it, then re-encoding), mitigating artifacts that arise when deforming latent images directly due to non-equivariant VAEs.
  • Residual Correction: A first-order correction term is added to address VAE reconstruction failures, especially when the input latent is far from the VAE's training distribution (e.g., early denoising steps), preventing washed-out colors.
• Design Choices & Improvements:
  • Model Choice: Stable Diffusion 3.5 Medium is primarily used for its high visual quality.
  • Time Travel: Repeating segments of the denoising process (typically 20% to 80% of steps) is found to improve image consistency and blending between different views.
  • Prioritizing a Single View: A user-defined portion of the final denoising steps can be dedicated solely to one view, encouraging the model to create coherent details for that view while effectively hiding the others, addressing the challenge of high-frequency details giving away hidden views in high-resolution images.
• Experimental Results:
  • Views Tested: The method generates illusions for various 2D transforms (vertical flip, 90°, 135° rotation) and complex 3D projections (conic mirror, cylindrical mirror, Nicéron’s lens).
  • Quantitative Performance: LookingGlass consistently outperforms or matches prior approaches (Visual Anagrams, Diffusion Illusions, SyncTweedies) in CLIP-based alignment/concealment scores and FID/KID metrics, especially for complex transformations.
  • Ablation Studies: Confirm the individual contributions of VAE encoding/decoding, residual correction, LPW, and time travel to improved image quality, detail preservation, and blending.
  • Qualitative Results: Demonstrations show high-quality generative anamorphoses, including a real-life cylindrical mirror example, validating the method's practical functionality.
  • User Study: A study with 27 participants showed a consistent preference for LookingGlass over prior methods regarding prompt fidelity, style adherence, and overall visual quality.
• Conclusion and Discussion:
  • LookingGlass enables feed-forward generation of high-quality, ambiguous anamorphic illusions with latent rectified flow models.
  • The key contributions are its extension to latent space models without artifacts and the introduction of Laplacian Pyramid Warping, which preserves fine details through extreme distortions.
  • LPW is also compatible with pixel diffusion models and has potential future applications in areas like generative mesh texturing and panorama synthesis.
  • Current limitations include the challenge of selecting effective prompts for high-quality illusions and higher computational expense compared to simpler feed-forward methods (though more efficient than optimization-based approaches).