arXiv Computer Vision By Yiqi Liang, Xiaodong Cun

SketchDirector: Complex Scene Sketching via Agentic Layout Reasoning and Detail Refinement

Read the original on arXiv Computer Vision →

SketchDirector is a new framework for generating complex scene sketches guided by text prompts. It uses a large language model to break a scene into draft assets described by Bézier curves, then an agentic reasoning loop plans their spatial arrangement with evolving memory, and finally a diffusion-based refinement module improves sketch quality while keeping geometry intact. The authors also release the MCSketch8K dataset, providing editable vector assets, compositional layouts, and object-level manipulation annotations to support future research.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers
Aug 10

SI-Edit: Toward Sketch-Instruction Guided Local Image Editing with Pixel-Level Precision

Despite rapid advances in generative models, achieving pixel-level precision in sketch-based image editing remains a persistent challenge, particularly for fine-grained local deformations. This gap stems primarily from the critical shortage of high-quality, publicly available benchmark datasets that jointly provide geometric constraints and semantic instructions.

arXiv AI
Jun 24

IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation

arXiv:2606. 24849v1 Announce Type: cross Abstract: Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved.

By Zixuan Li, Haokun Lin, Yicheng Xiao, Zhiwei Li, Xinyang Song, Zelong Zheng, Yong He, Heng Yao, Ke Ding, Chao Yu, Chuan Yuan, Qi Li, Zhenan Sun
Hugging Face Trending Papers
Jun 23

IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation

Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved. We attribute this limitation in part to the entanglement of structural planning and appearance rendering within a single conditioning stream.