PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation

• URL: http://arxiv.org/abs/2512.08534v1
• Date: Tue, 09 Dec 2025 12:31:00 GMT
• Title: PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation
• Authors: Zhangli Hu, Ye Chen, Jiajun Yao, Bingbing Ni,
• Abstract summary: Oil painting is a high-level medium that blends human abstract thinking with artistic expression.<n>Existing generation and editing techniques are often constrained by the distribution of training data.<n>We introduce a unified multimodal framework for oil painting generation and editing.
• Score: 47.72342715926692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Oil painting, as a high-level medium that blends human abstract thinking with artistic expression, poses substantial challenges for digital generation and editing due to its intricate brushstroke dynamics and stylized characteristics. Existing generation and editing techniques are often constrained by the distribution of training data and primarily focus on modifying real photographs. In this work, we introduce a unified multimodal framework for oil painting generation and editing. The proposed system allows users to incorporate reference images for precise semantic control, hand-drawn sketches for spatial structure alignment, and natural language prompts for high-level semantic guidance, while consistently maintaining a unified painting style across all outputs. Our method achieves interactive oil painting creation through three crucial technical advancements. First, we enhance the training stage with spatial alignment and semantic enhancement conditioning strategy, which map masks and sketches into spatial constraints, and encode contextual embedding from reference images and text into feature constraints, enabling object-level semantic alignment. Second, to overcome data scarcity, we propose a self-supervised style transfer pipeline based on Stroke-Based Rendering (SBR), which simulates the inpainting dynamics of oil painting restoration, converting real images into stylized oil paintings with preserved brushstroke textures to construct a large-scale paired training dataset. Finally, during inference, we integrate features using the AdaIN operator to ensure stylistic consistency. Extensive experiments demonstrate that our interactive system enables fine-grained editing while preserving the artistic qualities of oil paintings, achieving an unprecedented level of imagination realization in stylized oil paintings generation and editing.

Related papers

• Loomis Painter: Reconstructing the Painting Process [56.713812157283805]
  Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources lack interactivity and personalization.<n>We propose a unified framework for multi-media painting process generation with a semantics-driven style control mechanism.<n>We also build a large-scale dataset of real painting processes and evaluate cross-media consistency, temporal coherence, and final-image fidelity.
  arXiv  Detail & Related papers  (2025-11-21T16:06:32Z)
• Birth of a Painting: Differentiable Brushstroke Reconstruction [25.61763988336406]
  Painting embodies a unique form of visual storytelling, where the creation process is as significant as the final artwork.<n>Our approach produces realistic and stylized appearances, offering a unified model for digital painting.
  arXiv  Detail & Related papers  (2025-11-17T09:55:53Z)
• Every Painting Awakened: A Training-free Framework for Painting-to-Animation Generation [25.834500552609136]
  We introduce a training-free framework specifically designed to bring real-world static paintings to life through image-to-video (I2V) synthesis.<n>Existing I2V methods, primarily trained on natural video datasets, often struggle to generate dynamic outputs from static paintings.<n>Our framework enables plug-and-play integration with existing I2V methods, making it an ideal solution for animating real-world paintings.
  arXiv  Detail & Related papers  (2025-03-31T05:25:49Z)
• Neural-Polyptych: Content Controllable Painting Recreation for Diverse Genres [30.83874057768352]
  We present a unified framework, Neural-Polyptych, to facilitate the creation of expansive, high-resolution paintings. We have designed a multi-scale GAN-based architecture to decompose the generation process into two parts. We validate our approach to diverse genres of both Eastern and Western paintings.
  arXiv  Detail & Related papers  (2024-09-29T12:46:00Z)
• Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis [2.205829309604458]
  LPGen is a novel diffusion-based model specifically designed for landscape painting generation. LPGen introduces a decoupled cross-attention mechanism that independently processes structural and stylistic features. The model is pre-trained on a curated dataset of high-resolution landscape images, categorized by distinct artistic styles, and then fine-tuned to ensure detailed and consistent output.
  arXiv  Detail & Related papers  (2024-07-24T12:32:24Z)
• BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion [61.90969199199739]
  BrushNet is a novel plug-and-play dual-branch model engineered to embed pixel-level masked image features into any pre-trained DM. BrushNet's superior performance over existing models across seven key metrics, including image quality, mask region preservation, and textual coherence.
  arXiv  Detail & Related papers  (2024-03-11T17:59:31Z)
• ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration [51.205673783866146]
  We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. We utilize a texture extraction and distribution framework to transfer high-quality texture features between the degraded input and reference image. The StyleGAN-like architecture in our framework requires high-quality latent codes to generate realistic images.
  arXiv  Detail & Related papers  (2024-01-13T04:54:59Z)
• Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region [66.75826549444909]
  Stroke-based rendering aims to recreate an image with a set of strokes. We propose Compositional Neural Painter, which predicts the painting region based on the current canvas. We extend our method to stroke-based style transfer with a novel differentiable distance transform loss.
  arXiv  Detail & Related papers  (2023-09-07T06:27:39Z)
• Text-Guided Synthesis of Eulerian Cinemagraphs [81.20353774053768]
  We introduce Text2Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions. We focus on cinemagraphs of fluid elements, such as flowing rivers, and drifting clouds, which exhibit continuous motion and repetitive textures.
  arXiv  Detail & Related papers  (2023-07-06T17:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.