Visual Sketch: Methods & Applications

• Visual Sketch is a modality that uses sparse strokes or pixel annotations to encode the semantic and structural essence of visual concepts.
• Research employs raster, vector, and semantic representations with CNNs, transformers, and diffusion models for effective retrieval, generation, and editing.
• Evaluation using metrics like FID, CLIP accuracy, and temporal consistency demonstrates its practical application in AR, image synthesis, and interactive design.

A visual sketch is a symbolic, typically sparse, visual representation consisting of strokes or pixel-wise annotations that encode the semantic or structural essence of a visual concept, object, or scene. In computational vision, visual sketches function as both human-controllable inputs (e.g., for retrieval, synthesis, or design) and machine-interpretable signals (e.g., in neural models for recognition, generation, or editing). Recent research encompasses a variety of sketch modalities: raster versus vector, semantic (region-labeled) versus structural (contour or edge-only), and static (images) versus spatiotemporal (videos and AR environments). This article delineates the core principles, representations, computational methodologies, and key use cases underlying contemporary visual sketch research, with explicit reference to technical milestones from the arXiv literature.

1. Formal Models and Representations

A visual sketch can be mathematically characterized in multiple forms:

• Raster sketches: Sparse 2D images $I \in \mathbb{R}^{H \times W \times 3}$, comprising black or colored strokes on a white (or transparent) canvas (Bhunia, 2022).
• Vector sketches: Ordered sequences of 2D points or control vertices $S = \{p^1, ..., p^N\}$, or grouped into $K$ strokes $s_k = \{p^{i_k}, ..., p^{j_k}\}$. Bézier or spline representations are widely used for both static and temporally evolving sketches (Qu et al., 2023, Zheng et al., 2023, Arar et al., 12 Feb 2025).
• Semantic sketches: Pixel-wise or region-based label assignments $S : \Omega \rightarrow C$ over a normalized canvas, where $C$ is a finite semantic concept vocabulary (e.g., “person”, “sky”) (Rossetto et al., 2019, Lin et al., 11 Feb 2025).
• Latent/feature-based sketches: Continuous feature vectors produced inline within large vision-LLMs or AR systems, acting as internal tokens in multimodal reasoning architectures (Tong et al., 18 Dec 2025).

Partial or progressive sketches are prefixes of vector stroke sequences $S^k = \{p^1, ..., p^k\}$, often used for early retrieval or sketch completion (Bhunia, 2022).

2. Feature Extraction, Embedding, and Semantic Abstraction

Modern visual sketch systems employ both low-level and high-level feature encoding:

• Grid-based semantic embedding: Downsampling pixel- or region-labeled semantic sketches to $n \times n$ grids $G_{uv}$, each cell assigned a dominant label $l_{uv}$ (Rossetto et al., 2019).
• Concept-to-vector embedding: Mapping $l_{uv}$ via Word2Vec $w(l_{uv}) \in \mathbb{R}^m$ with subsequent low-dimensional projection (e.g., t-SNE $\phi(w(c)) \in \mathbb{R}^d$) to preserve semantic similarity (Rossetto et al., 2019).
• Semantic stroke saliency: CLIP-based and XDoG-based attention maps for data-driven initialization of vector control points and guiding abstraction in optimization loops (Zheng et al., 2023, Qu et al., 2023).
• Transformer or CNN encoders: For both raster and vector sketches, feature encoders $\mathcal{F}(\cdot)$ based on VGG, Inception, or CLIP backbone architectures extract high-level descriptors for retrieval, synthesis, or downstream prediction (Bhunia, 2022, Arar et al., 12 Feb 2025, Lin et al., 11 Feb 2025).
• Latent tokenization: Internally, LLMs may generate or consume continuous “sketch” tokens $\{s_1,...,s_K\} \subset \mathbb{R}^d$ interleaved with text tokens, enabling chain-of-thought visual reasoning purely in latent space (Tong et al., 18 Dec 2025).

In video sketch synthesis, per-frame Bézier strokes are harmonized using atlas-based UV mapping for temporal consistency (Zheng et al., 2023).

3. Computational Frameworks: Retrieval, Generation, and Reasoning

3.1. Retrieval

• Semantic Sketch-based Retrieval: Matching a user-defined semantic sketch (concept layout) $S$ against a library of images or video keyframes, both mapped to a high-dimensional vector $q \in \mathbb{R}^{n^2 \cdot d}$, followed by $L_1$ (Manhattan) distance-based kNN search (Rossetto et al., 2019).
• Partial Element-Based Retrieval: Classifying and localizing incomplete UI sketches, then matching against a structured screen database via composite cost functions over categories, shape (IoU), and position (Mohian et al., 2022).
• Reinforcement-Learning Driven Retrieval: On-the-fly retrieval using partial strokes $S^k$, optimizing retrieval rank as early as possible via policies learned by PPO (Bhunia, 2022).

3.2. Sketch-to-Image and Image-to-Sketch Generation

• Diffusion-Based Synthesis: Sketch-controlled image generation driven by a denoising diffusion process with cross-domain perceptual constraints ($\mathcal{L}_p$ for sketch faithfulness; $\mathcal{L}_i$ for identity) and optional classifier guidance for categorical specificity (Wang et al., 2023).
• Vectorized Sketch Generation: Transformer-decoder diffusion models (SwiftSketch) that operate on Bézier stroke control points, trained on synthetic image-sketch datasets derived via spatially controlled Score Distillation Sampling with depth-aware ControlNet (Arar et al., 12 Feb 2025).
• GAN-based Multi-Stage Sketch-to-Face Synthesis: Attribute-to-sketch mapping via CVAE (Stage 1), GAN-based sketch sharpening (Stage 2), and final face synthesis (Stage 3) conditioned on both sketch and attribute vectors (Di et al., 2017, Di et al., 2019).
• Scene and Style Disentanglement: Unsupervised mapping between scene-level sketches (edge maps) and real images by disentangling structure (content codes) and style (appearance codes), enabling fine-grained editing strictly via sketch input (Wang et al., 2022).

3.3. Creative and Functional Design

• Interactive ideation and recomposition: Systems (e.g. SketchConcept) that use freehand sketch plus language input to drive concept image generation via LLM/diffusion, followed by semantic segmentation, function mapping, and component-level editing through focused inpainting (Duan et al., 10 Aug 2025).
• Region semantic prompts and spatial conditioning: Rough region sketches mapped to semantic prompts (inferred with LLMs) and shape anchors (via SAM/Canny), guiding spatially controlled text-to-image diffusion pipelines (SketchFlex) (Lin et al., 11 Feb 2025).

3.4. Dynamic and Mixed-Modal Applications

• Augmented Reality and Responsive Visualization: Dynamic binding of hand-drawn sketches to object motion or physical phenomena in AR environments, using color-based and pose-based tracking, real-time expression parsing, and property binding (e.g., trajectory, angle, force, graph plotting) (Suzuki et al., 2020).
• Latent Visual Reasoning in MLLMs: Interleaving text and visual sketch tokens in a shared embedding space, with latent sketch tokens grounded via MSE to patch-aggregated vision encoder outputs, enabling visual “thinking” and unified multimodal chain-of-thought (Tong et al., 18 Dec 2025).

4. Evaluation Methodologies and Key Results

The technical literature reports a diverse array of evaluation protocols and metrics:

Application Domain	Key Metrics	Notable Performance/Findings
Retrieval	Retrieval time (ms), Top-k accuracy, User studies	Sub-1s queries on 1M+ items with semantic alignment (Rossetto et al., 2019)
Generation (sketch→img)	FID, IS, LPIPS, SSIM, Human preference	DiffSketching: FID=6.46 vs. GAN baselines 19–56 (Wang et al., 2023)
Vector sketching	CLIP Top-1 accuracy, MS-SSIM	SwiftSketch: 0.95 (seen), 0.70 (unseen) Top-1; 0.5s inference (Arar et al., 12 Feb 2025)
Video synthesis	Human ranking: temporal consistency, semantic abstraction	SVG-based output enables high abstraction & editing (Zheng et al., 2023)
AR/dynamic sketch	Latency, tracking accuracy, usability	20–30 FPS on iPad, real-time parameter binding (Suzuki et al., 2020)
Latent reasoning	MMVP, MMBench, Vision-centric benchmarks	SkiLa improves Qwen2.5-VL MMVP by +9.3 points (Tong et al., 18 Dec 2025)

Ablation studies consistently demonstrate the necessity of semantic consistency constraints, domain disentanglement, or explicit vectorization for both subjective and objective gains. User studies confirm improved creative control and intention alignment in interactive and design-focused systems (Lin et al., 11 Feb 2025, Duan et al., 10 Aug 2025).

5. Challenges, Limitations, and Prospective Directions

• Representation/Resolution Tradeoffs: Grid size $n$ (for semantic sketches) and stroke count or parametrization (for vector systems) control a tradeoff between spatial fidelity and computational/storage cost (Rossetto et al., 2019, Arar et al., 12 Feb 2025).
• Generalization across Styles and Domains: Diffusion-based methods exhibit strong domain transfer on schematic, QuickDraw-like sketches but degrade under highly abstract or OOD input; retraining or stronger augmentation partially mitigates this (Wang et al., 2023, Tas, 2023).
• Temporal Consistency: In video and AR contexts, temporal coherence is enforced by explicit regularization (atlas UV consistency, MLP smoothing), yet remains sensitive to noisy tracking or object drift (Zheng et al., 2023, Suzuki et al., 2020).
• Manual Label/Concept Expansion: For semantic sketch engines, adding new labels requires recalibrating the concept-to-vector embedding space (e.g., re-running t-SNE or switching to parametric projections) (Rossetto et al., 2019).
• Model Interpretability and Usability: While region-based and function-based decomposition frameworks offer granular editing, generation may hallucinate or misinterpret complex or rare input, especially with ambiguous sketches (Duan et al., 10 Aug 2025, Lin et al., 11 Feb 2025).
• Open Challenges: Explicit 3D sketching, richer multimodal reasoning, real-time AR/HMD integration, and large-scale user-centric evaluation remain open problems referenced by recent and pipeline-focused work (Tong et al., 18 Dec 2025, Suzuki et al., 2020).

6. Applications and System Integration

Representative applications of visual sketch frameworks include:

• Large-scale image and video retrieval in multimedia archives, using spatial-semantic sketching (Rossetto et al., 2019).
• Rapid interactive prototyping for product/industrial design, integrating sketch, language, segmentation, and GenAI (Duan et al., 10 Aug 2025).
• Creative content generation (raster/vector images, scene-level synthesis, video abstraction), pipeline integration with AR, and graph-driven visual analytics (Qu et al., 2023, Zheng et al., 2023, Suzuki et al., 2020).
• Fine-grained sketch-based image retrieval, few-shot learning, sketch editing for incremental domain adaptation, and cross-modal representation learning (Bhunia, 2022, Wang et al., 2023).
• Multimodal chain-of-thought reasoning and visual-world simulation in multimodal LLMs (Tong et al., 18 Dec 2025).

In summary, visual sketch is a rich, multi-representational modality that enables both direct human concept expression and powerful machine-mediated manipulation, search, and synthesis, with research converging toward scalable, semantically interpretable, and interactive frameworks across the vision–language–graphics continuum.