arXiv Computer Vision
Semantic-Structural Alignment for Generative Pictorial Charts
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Chart2SVG: Editable SVG Generation from Raster Chart Images
Chart2SVG is a multimodal large language model that transforms static raster chart images into editable SVGs enriched with semantic structure. By embedding chart‑specific semantic tokens into a vision‑language framework and training on the Beagle+ dataset of 33K distilled chart samples, the model captures both geometric primitives and their functional roles. The resulting SVGs are visually accurate and structurally consistent, and the accompanying Chart Structure Graph (CSG) exposes visual dependencies for interactive exploration, chart repurposing, and layout reuse.
arXiv AI
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CompArt: Operationalizing Aesthetic Alignment in Text-to-Image Generation via Principles of Art
CompArt introduces a new approach to aesthetic alignment in text-to-image generation by using the Principles of Art (PoA) such as Balance, Rhythm, and Emphasis to define explicit compositional constraints. The authors create a large dataset of 80,032 WikiArt images, each annotated with PoA analyses generated by a multimodal LLM, and present ArtDapter, a lightweight adapter that steers a pretrained diffusion model along ten PoA dimensions while preserving semantic fidelity. Experiments demonstrate that CompArt outperforms strong baselines in adhering to PoA controls under a dual evaluation protocol.
arXiv AI
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Appearance Pointers -- Multimodal Region Control of Diffusion Transformers
arXiv:2607. 19344v1 Announce Type: cross Abstract: Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone.
arXiv AI
Jun 4
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.
arXiv Computer Vision
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Text-to-Image Synthesis: A Decade Survey
This survey reviews 490 papers on text‑to‑image synthesis (T2I) over the past decade, covering key model families such as GANs, autoregressive models, and diffusion models. It organizes recent research by problem areas—controllability, personalization, editing, preference alignment, efficiency, safety, and spatial consistency—rather than strictly chronologically. The paper also examines unified multimodal models, datasets, evaluation metrics, and discusses applications, challenges, and future research directions in T2I.
arXiv Computer Vision
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Token-to-Token Alignment of Text Embeddings for Semantic Blending
Token-to-Token Alignment of Text Embeddings for Semantic Blending proposes a two‑stage framework that first rephrases text prompts into a shared structured form and then aligns token embeddings across prompts based on semantic similarity. By establishing explicit semantic correspondence between tokens, the method enables smooth, coherent transitions through simple linear interpolation, facilitating image blending and continuous editing. The authors demonstrate that properly aligned text embeddings reveal an implicit continuous semantic structure in text‑to‑image models, allowing semantic control without altering the generative model.