Unlayering Images: AI Recreates the Illustrator’s Process

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Author: Denis Avetisyan

A new approach uses artificial intelligence to predict the layered structure within images, paving the way for more effective vectorization and image editing.

The model predicts an ordering of compositional layers-analogous to an artist’s structuring of an image-that represents a learned depth for illustrations, paintings, and even realistic images, thereby enabling applications ranging from vectorization and intuitive editing to text-to-vector generation and 3D relief fabrication.
The model predicts an ordering of compositional layers-analogous to an artist’s structuring of an image-that represents a learned depth for illustrations, paintings, and even realistic images, thereby enabling applications ranging from vectorization and intuitive editing to text-to-vector generation and 3D relief fabrication.

This paper introduces ‘Illustrator’s Depth,’ a neural network that infers layer ordering for improved image decomposition and manipulation.

Decomposing flat images into editable layers remains a fundamental challenge in digital content creation, often requiring laborious manual effort. This paper introduces ‘Illustrator’s Depth: Monocular Layer Index Prediction for Image Decomposition’, a novel approach that reframes depth not as a physical property, but as a creative abstraction representing layer ordering. By training a neural network to predict this ‘illustrator’s depth’ directly from raster images, we achieve state-of-the-art performance in image vectorization and unlock new possibilities for applications like 3D relief generation and intuitive image editing. Could this method ultimately redefine how we interact with and manipulate digital imagery?

Beyond Superficial Depth: Unveiling Compositional Structure

Conventional techniques for discerning depth, such as those employed in 3D reconstruction and panoptic segmentation, are fundamentally geared towards understanding spatial relationships in the real world-identifying objects and their distance from a viewpoint. However, these methods often prove inadequate when applied to the realm of vector graphics. Unlike photographs or scans of physical scenes, digital artwork is defined by deliberate layers of abstraction and compositional choices. A simple depth map, for instance, cannot distinguish between overlapping shapes that are conceptually separate elements of a design-a foreground object versus a background color fill. Consequently, these techniques fail to capture the crucial structural information-the hierarchy and relationships between distinct visual components-that is essential for intuitive and powerful vector graphics editing. The inability to represent this compositional structure limits the potential for targeted modifications and creative control within applications like Adobe Illustrator.

Illustrator’s Depth moves beyond simply mapping artwork into a three-dimensional space; instead, it establishes a representation of structural layering – a fundamental understanding of which elements exist in front of, behind, or are otherwise relationally positioned to one another within a composition. This approach acknowledges that vector graphics are not inherently defined by depth in the same way as a photograph, but by the deliberate arrangement of shapes and objects. Consequently, Illustrator’s Depth doesn’t aim to recreate a perceived 3D scene, but to define the editable relationships between elements – essentially, a blueprint for how the artwork is constructed and can be manipulated. This distinction allows for more precise and intuitive editing, enabling users to directly address the compositional structure rather than attempting to infer it from simulated depth information.

The power of Illustrator’s Depth lies not in replicating three-dimensional space, but in establishing a framework for intuitive editing and creative manipulation of vector artwork. Conventional depth estimation techniques focus on calculating distance from the viewer, providing data about spatial arrangement; however, this information alone doesn’t dictate how an image can be altered. Illustrator’s Depth, conversely, constructs a structural map – a hierarchy of layers defining which elements sit ‘above’ or ‘below’ others – directly influencing an artist’s ability to select, isolate, and modify individual components. This distinction is paramount because it transforms a passive representation of depth into an active tool for design, granting users unprecedented control over the compositional elements of their illustrations and unlocking new possibilities for non-destructive editing.

Illustrator’s depth estimation uniquely reconstructs layered scenes with piecewise-flat regions, accurately preserving compositional ordering even for elements lacking true physical depth, unlike traditional monocular methods.
Illustrator’s depth estimation uniquely reconstructs layered scenes with piecewise-flat regions, accurately preserving compositional ordering even for elements lacking true physical depth, unlike traditional monocular methods.

Foundations for Depth: Training the System’s Understanding

Depth Pro utilizes a neural network architecture initialized with pre-trained weights to establish a foundational understanding of visual hierarchies. This transfer learning approach accelerates training and improves performance in predicting depth order within vector illustrations. The network accepts vectorized artwork as input and outputs a probability distribution representing the likelihood of each layer being in the foreground, effectively determining the perceived depth. Leveraging pre-trained weights reduces the need for extensive training data and computational resources while enhancing the model’s generalization capabilities to novel artwork compositions.

The MMSVG-Illustration Dataset is central to the training process for Illustrator’s depth prediction model. This dataset comprises a large collection of Scalable Vector Graphics (SVGs) specifically structured with multiple layers. Crucially, each SVG is meticulously curated to provide a definitive “ground truth” representation of the compositional order of elements – that is, which objects visually appear in front of or behind others. This layered structure allows for supervised learning, enabling the neural network to directly correlate visual features with accurate depth information. The consistent and reliable ground truth provided by the MMSVG-Illustration Dataset is essential for achieving high accuracy in the depth prediction model.

The SVGX-Core Dataset is utilized for validating the performance of the Illustrator Depth prediction model. This dataset consists of a collection of Scalable Vector Graphics (SVGs) specifically chosen to represent a wide range of artistic styles and compositional complexities. Utilizing SVGX-Core allows for a robust and generalized evaluation, assessing the model’s ability to accurately predict depth not just on familiar artwork, but across diverse visual representations. The dataset’s construction prioritizes consistent ground truth labeling, ensuring reliable metrics for assessing the model’s accuracy and identifying potential biases in its predictions.

By predicting depth, our method generates well-layered SVG images from complex scenes, as demonstrated by its ability to group disconnected elements into a single background layer while preserving detailed highlights.
By predicting depth, our method generates well-layered SVG images from complex scenes, as demonstrated by its ability to group disconnected elements into a single background layer while preserving detailed highlights.

From Vectorization to Relief: Expanding Creative Possibilities

Illustrator’s Depth improves the vectorization process by generating more accurate vector paths from raster images. Traditional vectorization algorithms often struggle with complex shapes and fine details, resulting in imprecise or fragmented vector graphics requiring significant manual cleanup. By incorporating depth prediction, Illustrator’s Depth refines the understanding of image structure, enabling the creation of vector representations that more faithfully capture the original raster data. This leads to reduced node counts, cleaner paths, and improved editability of the resulting vector artwork, minimizing the need for post-processing and accelerating the design workflow.

Illustrator’s Depth extends graphic creation beyond two-dimensional space by enabling relief generation, a process which constructs three-dimensional surfaces from existing 2D artwork. This functionality leverages the predicted depth information – calculated for each element within the 2D image – and interprets this data as height values. Consequently, a 2D illustration can be transformed into a simulated 3D relief, effectively adding a perceived third dimension by manipulating visual depth cues. This process allows for the creation of 3D-like assets directly within Illustrator, without requiring separate 3D modeling software or complex sculpting processes.

Illustrator’s Depth demonstrates a high degree of accuracy in depth ordering, achieving over 98% consistency as measured on the MMSVG dataset. This performance metric indicates the system’s ability to correctly determine the relative depth of elements within an image, which is crucial for maintaining visual coherence and enabling precise control over object arrangement. Comparative analysis reveals that Illustrator’s Depth surpasses the layering quality of current state-of-the-art methods, resulting in more accurate and visually logical compositions when converting 2D artwork into layered representations or 3D relief maps.

Our method generates editable, depth-ordered vector graphics that closely match input images with superior layering and visual fidelity compared to existing vectorization techniques.
Our method generates editable, depth-ordered vector graphics that closely match input images with superior layering and visual fidelity compared to existing vectorization techniques.

A Glimpse into the Future: Text-to-Vector and Beyond

The advent of sophisticated text-to-vector graphics hinges on a growing integration of depth-aware technologies, notably Adobe Illustrator’s Depth feature, within AI-driven pipelines. This allows users to generate intricate artwork simply by providing textual prompts, bypassing the need for manual vector creation. By understanding spatial relationships and object hierarchies-information gleaned from Illustrator’s Depth-algorithms can now produce vectors with improved accuracy and visual coherence. The system translates natural language into structured graphical representations, enabling the creation of detailed illustrations, icons, and designs with minimal user effort and offering a pathway to democratize complex visual content creation.

Recent advancements in text-to-vector graphics rely heavily on the interplay between sophisticated sampling methods and structural understanding of images. Techniques such as Score Distillation Sampling, which refines generated vectors based on a scoring function, are notably enhanced when combined with Neural Path Representations and NeuralSVG. These methods benefit significantly from the depth information provided by tools like Illustrator’s Depth, enabling the AI to interpret and recreate complex scenes with greater accuracy. By understanding the spatial relationships and layering within an image, the algorithms can generate vectors that more faithfully represent the original intent, resulting in higher-quality vectorizations and improved visual fidelity – a crucial step toward truly intelligent image creation.

The convergence of AI and vector graphics tools is poised to redefine artistic workflows, offering creators a new level of control and streamlining the design process. Recent advancements demonstrate that integrating technologies like Illustrator’s Depth into AI pipelines not only accelerates vector creation from text prompts but also demonstrably enhances the quality of the resulting images. Objective metrics, including Structural Similarity Index Measure (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS), consistently reveal significant improvements in visual fidelity when these synergistic approaches are employed in image vectorization tests. This suggests a future where artists can leverage the power of AI to rapidly prototype, iterate, and realize complex visual concepts with greater precision and efficiency, effectively augmenting human creativity rather than replacing it.

A generative pipeline leveraging Nano Banana synthesizes layered vector graphics from input images and textures, enabling depth-aware editing features like recoloring and object insertion.
A generative pipeline leveraging Nano Banana synthesizes layered vector graphics from input images and textures, enabling depth-aware editing features like recoloring and object insertion.

The pursuit of ‘Illustrator’s Depth’ exemplifies a drive toward elegant solutions in image decomposition. The network’s ability to predict layer ordering isn’t merely a technical feat, but a demonstration of understanding how visual information is intrinsically structured. As Andrew Ng once stated, “AI is the new electricity.” This paper doesn’t simply apply AI; it harmonizes with the inherent structure of visual data, much like a well-tuned instrument. The concept of inferring layered structure from a single image, enabling improved vectorization, highlights that good design – in this case, a sophisticated neural network – whispers its capabilities rather than shouting them. It’s a subtle power born of deep comprehension.

What’s Next?

The pursuit of layered representation, as exemplified by ‘Illustrator’s Depth,’ inevitably bumps against the inherent ambiguity of projection. The network successfully infers layers, yet the true test isn’t mere detection, but graceful handling of occlusion and self-similarity – the visual world rarely cooperates with clean segmentation. Future iterations must move beyond pixel-level prediction and embrace relational reasoning; understanding how layers interact is more valuable than simply where they are.

One senses a path toward editing, not rebuilding. Refactoring, a gentle rearrangement of existing elements, promises more intuitive creative control than wholesale vectorization. However, achieving this demands a shift in evaluation metrics; current benchmarks reward faithful reconstruction, not elegant simplification. Beauty scales-clutter doesn’t. The field must learn to prioritize concision and aesthetic coherence.

Ultimately, the value of ‘Illustrator’s Depth’ – and similar approaches – lies not in replicating the output of a human artist, but in providing tools that amplify creative intent. The network’s limitations are, ironically, its greatest opportunity. For it is in navigating those constraints that genuinely novel forms of expression might emerge.

Original article: https://arxiv.org/pdf/2511.17454.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-24 23:44