SketchFlow is a new generative framework for creating high‑quality vector sketches from text prompts. It uses a Gaussian Mixture Model prior in the CLIP latent space and an Optimal Transport Conditional Flow Matching model to map this prior to sketch features, which are then decoded by a Hybrid Diffusion Decoder combining 1D U‑Net and Transformer architectures. The approach achieves superior visual quality and human‑like drawing styles, and supports zero‑shot synthesis for unseen concepts and smooth semantic interpolation.
By Jin Zhou, Hongliang Yang, Pengfei Xu, Hui Huang
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
The paper presents a method for reconstructing 3D wireframes from single 2D line drawings by treating the task as conditional depth estimation. It uses a Latent Diffusion Model with spatial conditioning on the sketch and optional partial-depth cues to predict depth for each drawn pixel, producing a 3D wireframe that faithfully matches the visible strokes. Trained on over three million synthetic image‑depth pairs from CAD wireframes, the approach achieves a best‑of‑five normalized depth error of 3.9% and demonstrates robust performance across varying shape complexities.
By Elton Cao, Hod Lipson
arXiv:2603.14474v2 Announce Type: replace
Abstract: Sketch techniques have been extensively studied in recent years and are especially well-suited to data streaming scenarios, where the sketch summar...
By Xinyu Yuan, Yan Qiao, Zonghui Wang, Wenzhi Chen
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.
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.
Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important for part reproduction and manufacturing process automation. However, existing studies either process only vector drawings or are limited to specific domains, and fail to explicitly connect dimensional annotations to geometric information, limiting their use of dimensional information for 3D Parametric CAD sequences recovery.
arXiv:2607. 04119v1 Announce Type: cross Abstract: Reconstructing Computer-Aided Design (CAD) modeling sequences from images is crucial for preserving design intent and supporting parametric editing.
By Zhaopeng Feng, Chen Zhi, Xuhong Zhang, Zhengwen Feng, Xinkui Zhao
arXiv:2606. 11837v1 Announce Type: cross Abstract: Open-vocabulary scene sketch semantic segmentation aims to assign dense semantic labels to sparse line drawings based on flexible category vocabularies specified at inference time, without relying on pixel-level annotations during training.
By Liwen Yi, Xianlin Zhang, Yue Zhang, Yue Ming, Xueming Li
arXiv:2604.22875v3 Announce Type: replace-cross
Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-languag...
By Brandon Collins, Logan Bolton, Hung Huy Nguyen, Mohammad Reza Taesiri, Trung Bui, Anh Totti Nguyen
arXiv:2605. 26089v2 Announce Type: replace-cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens.
By Wei Song, Tianhang Wang, Yitong Chen, Tong Zhang, Zuxuan Wu, Min Li, Jiaqi Wang, Kaicheng Yu
arXiv:2603.29852v2 Announce Type: replace-cross
Abstract: We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, comp...
By Joan Rodriguez, Haotian Zhang, Abhay Puri, Haoran Dai, Tianyang Zhang, Meng Lin, Rishav Pramanik, Xiaoqing Xie, Marco Terral Rodriguez, Darsh Kaushik, Aly Shariff, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli