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Concept Decomposition for Visual Exploration and Inspiration

Yael Vinker, Andrey Voynov, Daniel Cohen-Or, Ariel Shamir

arXiv:2305.18203v2cs.CV

TL;DR

Existing visual concepts often need to be separated into aspects so designers can draw inspiration without copying them wholesale. The paper learns a hierarchical tree of aspect embeddings in a pretrained text-to-image model, enabling visual sampling and combinations. Visual results show derived concepts and new design ideas across challenging examples, while deeper trees and larger branching remain constrained.

  • Problem

    Design inspiration requires extracting selected aspects from existing visual concepts rather than simply replicating whole designs.

    Source: §1 · pp. 1–2

  • Method

    The method recursively learns a binary tree of embedding-based sub-concepts, using parent reconstruction and coherency constraints to represent visual aspects.

    Source: §1 · p. 2, §4.1 · p. 4

  • Results

    Visual results on challenging concepts demonstrate aspect discovery, exploration of derived concepts, and generation of new design ideas.

    Source: §1 · p. 3, §5 · p. 6, §7 · p. 8

  • Takeaways & Limitations

    Learned aspects can be sampled independently, combined within or across trees, and inserted into natural-language prompts for novel visual generation.

    Source: §7 · p. 8

    The method can struggle with deeper trees and nodes with more than two children, sometimes stopping when embeddings become too simple, incoherent, or out of distribution.

    Source: §6 · p. 8

Abstract

from arXiv · show

A creative idea is often born from transforming, combining, and modifying ideas from existing visual examples capturing various concepts. However, one cannot simply copy the concept as a whole, and inspiration is achieved by examining certain aspects of the concept. Hence, it is often necessary to separate a concept into different aspects to provide new perspectives. In this paper, we propose a method to decompose a visual concept, represented as a set of images, into different visual aspects encoded in a hierarchical tree structure. We utilize large vision-language models and their rich latent space for concept decomposition and generation. Each node in the tree represents a sub-concept using a learned vector embedding injected into the latent space of a pretrained text-to-image model. We use a set of regularizations to guide the optimization of the embedding vectors encoded in the nodes to follow the hierarchical structure of the tree. Our method allows to explore and discover new concepts derived from the original one. The tree provides the possibility of endless visual sampling at each node, allowing the user to explore the hidden sub-concepts of the object of interest. The learned aspects in each node can be combined within and across trees to create new visual ideas, and can be used in natural language sentences to apply such aspects to new designs.

1. Introduction

The paper asks whether machines can decompose visual concepts into aspects that support creative exploration rather than simple replication. It represents these aspects in a hierarchical tree and uses them to generate and combine new visual ideas.

Motivation

  • Design inspiration often requires extracting selected aspects from existing visual concepts instead of copying entire designs.Examples include nature-inspired patterns and combinations of architectural, geological, and ceramic concepts.Source: §1 · pp. 1–2, Figure 2 · p. 2

Research question

  • The paper investigates whether large vision-language models can decompose concepts into visual aspects and provide unexpected examples for design inspiration.Source: §1 · p. 2

Method

  • The method builds a binary tree whose learned embedding nodes represent progressively separated aspects of an original concept.For example, a concept can be separated into a wooden saucer, a bear drawing, the general bear concept, and a texture.Source: §1 · p. 2
  • Child embeddings are optimized to reconstruct their parent jointly while remaining coherent and distinct through CLIP-based consistency measurement.The aspects are learned implicitly without prescribing a split such as shape or texture.Source: §1 · p. 2

Exploration

  • The learned aspects can be combined within or across trees and inserted into natural-language prompts to generate aspect-based variations.Source: §1 · pp. 2–3, Figure 3 · p. 2

Results

  • Visual results on challenging concepts show that the approach discovers derived concepts and supports new design ideas.Source: §1 · p. 3

2. Previous Work

Prior work supports creativity through visual search, evolutionary generation, large vision-language models, and personalization. This paper differs by extracting multiple aspects from existing visual concepts and generating inspiration from them.

Design and Modeling Inspiration Creativity

  • Creativity research characterizes ideation as recombining prior knowledge, while design tools commonly help users collect, arrange, and search visual or textual exemplars.The paper instead focuses on extracting aspects from a given concept and generating new images for inspiration.Source: §2 · p. 3
  • Evolutionary creativity systems mainly target 3D content generation and generally do not decompose aspects from existing concepts.Source: §2 · p. 3

Large Language-Vision Models

  • Large language-vision and diffusion models provide visual and semantic priors that can support artistic tasks and transform existing concepts into new ones.Source: §2 · p. 3

Personalization

  • Personalized text-to-image methods learn user-provided concepts and have enabled intuitive editing and more accessible design workflows.Source: §2 · p. 3
  • The paper adopts embedding-based personalization because its goal is aspect capture rather than reconstruction accuracy.An alternative that modifies the denoising network is more robust but requires more memory and a separate model for each object.Source: §2 · p. 3

3. Preliminaries

The method builds on latent diffusion and textual inversion: images are encoded into latent space, noise is predicted conditionally, and learned token embeddings represent visual concepts. These components provide the basis for optimizing aspect embeddings.

Latent Diffusion Models

  • Latent diffusion models encode images into latent vectors and train a denoising network to predict noise in the learned latent space.Stable Diffusion applies this process to the latent space of a pretrained image autoencoder.Source: §3 · p. 3
  • At inference, a random latent code is iteratively denoised under a text condition and decoded into an image.Source: §3 · p. 3

Binary reconstruction

  • The binary reconstruction stage generates parent-concept training images, adds two child embeddings initialized from “object,” and optimizes them with the LDM loss.Source: Figure 4 · p. 4

Text embedding

  • Text prompts are tokenized, mapped through a dictionary of embeddings, processed by a text transformer, and supplied as conditioning to the denoising network.Source: §3 · p. 4

Textual Inversion

  • Textual inversion adds a new embedding linked to a token and optimizes it with the latent diffusion loss so a small image set is represented by that concept.The embedding is optimized using noised image latents, timestep samples, noise, and neutral context prompts.Source: §3 · p. 4

4. Method

The method builds a binary exploration tree whose learned embeddings represent distinct visual aspects of an input concept. Child embeddings are optimized for joint reconstruction, individual coherency, and sibling distinctness, with consistency-based seed selection filtering unstable results.

4. Method

  • A binary tree represents a concept through learned vector embeddings that generate varied image examples for navigating its visual aspects.Each tree level exposes further aspects, while each node supports independent visual sampling.Source: §4 · p. 4

4.1. Tree Construction

  • Child embeddings are optimized so that their combination reconstructs the parent concept while separating it into two aspects.The process initializes both children with “object” and gradually adapts them to the parent image set.Source: §4.1 · pp. 4–5, Figure 5 · p. 5
  • The reconstruction procedure trains two child embeddings from parent images using a pretrained Stable Diffusion UNet conditioned on text containing the child placeholders.Images are encoded into noisy latents, and the prediction loss is backpropagated to the child vectors.Source: §4.1 · p. 5
  • The learned embeddings can still produce individually incoherent sub-concepts even when their pair captures the parent concept well.Incoherence is associated with generated image sets having no clear common concept.Source: §4.1 · p. 5, Figure 6 · p. 5
  • The consistency filter addresses incoherent embeddings, which can arise because textual-inversion vectors may fall outside common-word distributions and depend on seeds and iteration choices.The authors use k=4 seeds and 200 initial optimization steps before selecting a pair and continuing optimization for 1500 iterations.Source: §4.1 · pp. 5–6
  • A CLIP-based consistency test selects sibling pairs whose image sets are individually coherent and relatively distinct from one another.The method generates 40 images per candidate embedding and compares within-set and cross-set CLIP similarities.Source: §4.1 · pp. 5–6, Figure 7 · p. 6

5. Results

The method creates hierarchical visual concept trees whose learned aspects support intra-tree, inter-tree, and text-based exploration, while evaluations assess consistency, reconstruction, separation, and aspect relevance.

5. Results

  • The learned tree nodes capture distinct sub-concepts that follow a gradual hierarchical decomposition without external guidance.Examples include concepts such as a cat, a sculpture, and a unique texture.Source: §5 · p. 6

5.1. Applications

  • Combining node embeddings within a tree produces controlled variations that add or remove specific aspects of the original concept.Examples include removing a cat-related sub-concept or generating a flat cat with the original object's body and texture.Source: §5.1 · p. 6
  • Aspects from independently learned trees can be combined by placing their placeholder words together, producing new cross-concept visual combinations.The examples combine a saucer drawing with a mug creature and a cat sculpture with Buddha stone.Source: §5.1 · p. 6, Figure 8 · p. 7
  • Text-based aspect composition enables new designs, whereas ordinary Textual Inversion may dominate, ignore, or include all aspects and thereby narrow exploration.The comparison includes chair and dress generations driven by learned aspects.Source: §5.1 · p. 7, Figure 9 · p. 7

5.2. Evaluations

  • 82.3% of CLIP-based consistency comparisons matched human choices across 15 image-set pairs from 9 objects.The perceptual study included 35 participants.Source: §5.2 · p. 7
  • 0.8 average reconstruction consistency and 0.59 average sibling consistency indicate that child combinations resemble their parent while siblings remain separated but related.The quantitative evaluation used 13 concepts and corresponding trees.Source: §5.2 · pp. 7–8
  • 87.8% recognition rates in a 35-participant study show that sampled aspects were generally recognized as originating from their source objects.The evaluation used 5 objects and 3 random aspects per object.Source: §5.2 · p. 8

6. Limitations

The method is constrained by input-image quality, difficult or overlapping aspect structure, limited tree depth and branching, and substantial optimization time.

6. Limitations

  • Background leakage can occur when training images lack varied perspectives and backgrounds, causing a sibling node to capture background information instead of the object.This requirement also exists in.Source: §6 · p. 8
  • Some decompositions produce incomprehensible aspects, dominant sub-concepts, or large overlap between sibling concepts.These failures can occur even when coherency is maintained.Source: §6 · p. 8
  • The authors suggest additional regularization or more robust personalization methods as possible ways to address these limitations.Source: §6 · p. 8
  • The method has difficulty creating deeper trees or nodes with more than two children and may stop when sub-concepts become too simple or incoherent.The authors associate this with possible embedding drift toward out-of-distribution codes.Source: §6 · p. 8
  • Decomposing a node can take approximately 40 minutes on a single A100 GPU.The authors expect progress in textual inversion optimization to improve this limitation.Source: §6 · p. 8

7. Conclusions

The paper presents implicit aspect decomposition as a visual exploration space for generating variations, combining concepts, and applying learned aspects to new designs.

7. Conclusions

  • The method implicitly decomposes visual concepts into aspects that form an inspiring visual exploration space.Source: §7 · p. 8
  • Learned aspects support numerous subject variations, combinations across objects, and natural-language generation of novel concepts.Source: §7 · p. 8
  • Implicit learning requires no external separation guidance and can produce surprising creative representations from the latent space of large vision-language models.The method was demonstrated on a variety of challenging concepts.Source: §7 · p. 8

A. Implementation details

The implementation uses Textual Inversion and compares it with Textual Inversion and Extended Textual Inversion baselines for specific-aspect blending.

A. Implementation details

  • The method relies on the diffusers implementation of Textual Inversion with Stable Diffusion v1.5, using batch size 2, learning rate 0.004, and four optimization seeds.Ten images per node are selected from 40 candidates using the proposed CLIP consistency measure.Source: A. Implementation details · p. 13

B. Baselines

  • The baseline comparison targets blending specific aspects from source objects, including a wooden saucer with a creature and a statue part with cat-sculpture style.Source: B. Baselines · p. 13, Figure 13 · p. 14
  • XTI separates geometry and style by assigning low-resolution layers to shape and high-resolution layers to appearance.The baseline uses independent textual inversions and different layer splits.Source: B. Baselines · p. 13
  • Textual Inversion blends attributes, while XTI transfers broader appearance or texture but fails to isolate shape; the proposed method combines the two selected aspects naturally.The comparison uses Textual Inversion [11] and Extended Textual Inversion [45].Source: B. Baselines · p. 13

C.1. Binary Tree

The method uses a binary tree because adding more than two children increases complexity and can produce less consistent aspects. Experiments show that two-child optimization generally yields more coherent decompositions than three-child optimization.

C.1. Binary Tree

  • More than two children increase runtime and produce more aspects to visualize and navigate, reaching 12 aspects after two levels in a simple three-child tree.The authors identify complexity and consistency as the two reasons for choosing a binary structure.Source: §C.1 · p. 14
  • Optimizing more than two nodes at once often leaves one node inconsistent or focused on irrelevant content such as background.The two-node case better separates the parent concept while preserving coherent child concepts.Source: §C.1 · p. 14
  • Two-child optimization produces coherent decompositions, whereas three-child optimization can yield an inconsistent or incomprehensible third aspect.This trend remains across additional random seeds in the red-teapot example.Source: §C.1 · p. 14, Figure 14 · p. 15
  • Across trees, the two most consistent nodes achieved average consistency scores of 0.804 and 0.742, versus 0.633 for the third, indicating two were consistent on average.The third node may contain incoherent information.Source: §C.1 · p. 15

C.2. Timestep Sampling

The method favors larger diffusion timesteps during optimization rather than sampling timesteps uniformly. The ablation shows that uniform sampling harms both reconstruction and decomposition quality.

C.2. Timestep Sampling

  • The selected timestep strategy skews sampling toward larger t values and plays a significant role in the method’s success.The approach is adopted from ReVersion [19].Source: §C.2 · p. 15
  • Figure 15 compares uniform timestep sampling on the left with the sampling approach selected from ReVersion [19] on the right, which favors larger values of t.Source: §C.2 · p. 15, Figure 15 · p. 15
  • Uniform timestep sampling negatively affects reconstruction quality and decomposition quality compared with the selected larger-timestep-favoring approach.The comparison uses the same seed and settings after 500 iterations; for the cat sculpture, uniform sampling makes v1 unrelated to the original concept.Source: §C.2 · p. 15, Figure 15 · p. 15

C.3. Consistency Test

The consistency test evaluates whether each node is internally coherent and whether sibling nodes are distinct. Results support the measure’s usefulness while showing that separation becomes harder deeper in the tree and that seed choice can matter.

C.3. Consistency Test

  • Seed variability can produce meaningful failures, including an inconsistent v2 or a tree dominated by one node.The consistency measurement selects the seed marked in green among the tested alternatives.Source: §C.3 · p. 16, Figure 18 · p. 16
  • The consistency test uses self-consistency for within-node coherence and sibling consistency for similarity between sibling nodes.Splitting is stopped when siblings are too similar or a node is internally inconsistent.Source: §C.3 · pp. 16–17
  • Self-consistency scores remain high across most nodes, but one node reaches 0.66 and is considered inconsistent on the reported scale.The root node scores 0.89, the highest possible score in the described setting.Source: §C.3 · pp. 16–17
  • Sibling consistency is low at the first level, with scores of 0.6 and 0.61, but generally increases at the second level as separation quality decreases.The sibling scores track visually similar node pairs in the example trees.Source: §C.3 · p. 17, Figure 19 · p. 17
  • Sibling consistency rises from the first to the second tree level, indicating that splits become less distinct as components become simpler.Average self-consistency remains high, while deeper splits are increasingly difficult.Source: §C.3 · p. 17, Table 1 · p. 17

C.4. Perceptual Study

  • The perceptual study collected responses from 35 participants for consistency and aspect-relevance questions across image-set comparisons.Participants answered 15 questions or evaluated 15 image-set pairs, depending on the experiment.Source: Figure 20 · p. 17, §C.4 · p. 18, Figure 21 · p. 18

D. Additional Qualitative Results

Additional results illustrate the exploration tree across many objects and show intra-tree combinations, inter-tree combinations, and text-based generation. These examples demonstrate the range of visual exploration outputs supported by the method.

D. Additional Qualitative Results

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