arXiv:2608. 12064v1 Announce Type: cross Abstract: We invert the typical formulation of sketch generation: instead of drawing strokes in order, we predict a 2D field that defines the order in which strokes are drawn.
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 Machine Learning.
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.
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.
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.
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...
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.