arXiv Computer Vision
PaintCopilot: Modeling Painting as Autonomous Artistic Continuation
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arXiv Computer Vision
Oct 22
Painting Change Map as guidance for generating painting processes
The paper introduces the Painting Change Map, a spatio‑temporal control mechanism that tracks normalized structural changes across a 1,000‑timestep continuum for dynamic video generation of paintings. By encoding localized visual differences into an RGB space, it provides fine‑grained guidance for both forward painting and reverse unpainting processes. Experiments show that this approach outperforms existing control baselines and matches or exceeds state‑of‑the‑art painting methods, especially under oracle conditions.
arXiv Computer Vision
Oct 22
Loomis Painter: Learning Painting Processes by Reversing Image-to-Video Generation
Loomis Painter is a framework that learns painting processes by training on reversed image‑to‑video sequences, preserving reference‑attention behavior and avoiding long‑range dependencies that disrupt pretrained models. It introduces a media‑transfer strategy to decouple input appearance from the target process, enabling generation across different artistic media. Evaluated on a curated dataset of real‑world painting processes, the method outperforms baselines on LPIPS, DINO, and CLIP metrics and receives consistent preference in expert and non‑expert user studies.
arXiv AI
Aug 10
Beyond Starry Night: Shortcut-Aware Control-State Planning for Artist-Grounded Text to Image Generation
arXiv:2608. 06751v1 Announce Type: cross Abstract: Artist-grounded image generation requires more than appending an artist name to a prompt.
arXiv Machine Learning
Oct 15
How Far Can Coding Agents Go in Visual Generation?
The paper investigates general‑purpose coding agents that generate images by writing and executing programs, contrasting them with conventional generative models. On the AgentDrawBench benchmark, these agents excel at preserving structure and meeting precise visual constraints, achieving competitive performance in tasks where compositional accuracy outweighs photorealism, though they fall short in perceptual richness. The study further shows that the agents’ visual code can serve as an external language of thought, retaining substantial information for visual question answering and suggesting an internal, compositionally structured representation that guides visual operations across different drawing interfaces.
arXiv Computer Vision
Oct 5
ProgressNet: Sketching and Prompting with a Frozen Text-to-Image Model
ProgressNet is a training‑free framework that enables a frozen text‑to‑image model to follow a sketching session in real time. It allows strokes to be added or erased, prompts to be revised, and produces an updated image in about a second per turn without adding new parameters. The method uses three inference‑time mechanisms—Previous‑Concept Memory, Layer‑Selective K/V Injection, and Banded Adaptive Control—to maintain fidelity and coherence across sketch domains, outperforming existing approaches especially when sketches are partially erased.
arXiv AI
Sep 1
Conducting Stylistic Analysis of Paintings through an Art-History Agent
arXiv:2608.29644v1 Announce Type: cross Abstract: Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art his...