Style-Aware Gloss Control for Generative Non-Photorealistic Rendering

• URL: http://arxiv.org/abs/2602.16611v2
• Date: Thu, 19 Feb 2026 08:15:07 GMT
• Title: Style-Aware Gloss Control for Generative Non-Photorealistic Rendering
• Authors: Santiago Jimenez-Navarro, Belen Masia, Ana Serrano,
• Abstract summary: We study how gloss and artistic style are represented in learned models.<n>We introduce a lightweight adapter that connects our style- and gloss-aware latent space to a latent-diffusion model.
• Score: 3.6258775536484347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can infer material characteristics of objects from their visual appearance, and this ability extends to artistic depictions, where similar perceptual strategies guide the interpretation of paintings or drawings. Among the factors that define material appearance, gloss, along with color, is widely regarded as one of the most important, and recent studies indicate that humans can perceive gloss independently of the artistic style used to depict an object. To investigate how gloss and artistic style are represented in learned models, we train an unsupervised generative model on a newly curated dataset of painterly objects designed to systematically vary such factors. Our analysis reveals a hierarchical latent space in which gloss is disentangled from other appearance factors, allowing for a detailed study of how gloss is represented and varies across artistic styles. Building on this representation, we introduce a lightweight adapter that connects our style- and gloss-aware latent space to a latent-diffusion model, enabling the synthesis of non-photorealistic images with fine-grained control of these factors. We compare our approach with previous models and observe improved disentanglement and controllability of the learned factors.

Related papers

• An Exploratory Study on Abstract Images and Visual Representations Learned from Them [27.912006389955945]
  We study the reasons behind this performance gap and investigate how much high-level semantic content can be captured at different abstraction levels.<n>We then train and evaluate conventional vision systems on HAID across various tasks including classification, segmentation, and object detection.<n>We also discuss if the abstract image can be considered as a potentially effective format for conveying visual semantic information and contributing to vision tasks.
  arXiv  Detail & Related papers  (2025-09-17T16:30:34Z)
• Learning Artistic Signatures: Symmetry Discovery and Style Transfer [8.288443063900825]
  There is no undisputed definition of artistic style.<n>Style should be thought of as a set of global symmetries that dictate the arrangement of local textures.<n>We show that by considering both local and global features, using both Lie generators and traditional measures of texture, we can quantitatively capture the stylistic similarity between artists better than with either set of features alone.
  arXiv  Detail & Related papers  (2024-12-05T18:56:23Z)
• Opt-In Art: Learning Art Styles Only from Few Examples [50.60063523054282]
  We show that it is possible to adapt a model trained without paintings to an artistic style, given only few examples.<n>Surprisingly, our findings suggest that high-quality artistic outputs can be achieved without prior exposure to artistic data.
  arXiv  Detail & Related papers  (2024-11-29T18:59:01Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques [11.286457041998569]
  We show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model.
  arXiv  Detail & Related papers  (2023-08-29T13:15:13Z)
• Inversion-Based Style Transfer with Diffusion Models [78.93863016223858]
  Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image.
  arXiv  Detail & Related papers  (2022-11-23T18:44:25Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer [78.91753256634453]
  We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
  arXiv  Detail & Related papers  (2021-10-30T01:59:39Z)
• Style and Pose Control for Image Synthesis of Humans from a Single Monocular View [78.6284090004218]
  StylePoseGAN is a non-controllable generator to accept conditioning of pose and appearance separately. Our network can be trained in a fully supervised way with human images to disentangle pose, appearance and body parts. StylePoseGAN achieves state-of-the-art image generation fidelity on common perceptual metrics.
  arXiv  Detail & Related papers  (2021-02-22T18:50:47Z)
• Learning Portrait Style Representations [34.59633886057044]
  We study style representations learned by neural network architectures incorporating higher level characteristics. We find variation in learned style features from incorporating triplets annotated by art historians as supervision for style similarity. We also present the first large-scale dataset of portraits prepared for computational analysis.
  arXiv  Detail & Related papers  (2020-12-08T01:36:45Z)

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.