Rujie Yin
This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural network.
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Expanding the Content-Style Frontier: a Balanced Subspace Blending Approach for Content-Style LoRA Fusion
针对文本到图像扩散模型中风格增强导致内容特征丢失的问题，提出内容-风格子空间混合与平衡损失方法，有效拓宽内容-风格边界，在定性和定量评估中均实现更优的内容-风格权衡。
Neural style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here attempt to separate the representations for styles and contents, and propose a generalized style transfer network consisting of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are used to extract the style and content factors from the style reference images and content reference images, respectively. The mixer employs a b…
Image style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here propose a unified style transfer framework for both character typeface transfer and neural style transfer tasks leveraging style and content separation. A key merit of such framework is its generalizability to new styles and contents. The overall framework consists of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are use…
Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
针对扩散变换器在风格迁移中内容与风格特征纠缠的问题，提出多阶段课程持续学习框架Style-CCL，通过从语义到纹理风格、从干净到合成数据的分阶段训练，并采用随机记忆排练防止灾难性遗忘，在风格相似性、内容一致性和美学质量上达到最优。
Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styled drawings for a given text description where a user can specify a desired drawing style using a sample image. Inspired by a theory in art that style and content are generally inseparable during the creative process, we propose a coupled approach, known here as StyleCLIPDraw, whereby the drawing is…
One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from style…
QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit
QwenStyle通过课程持续学习框架，在保留内容的同时实现高质量风格迁移，达到最先进的性能。
SCAdapter: Content-Style Disentanglement for Diffusion Style Transfer
SCAdapter通过CLIP图像空间实现内容与风格的解耦，提升扩散模型在逼真图像迁移中的效果和效率。
Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced content and style are is crucial in evaluating the quality of stylized images. In this paper, we propose an end-to-end two-stream Fully Convolutional Networks (FCNs) aiming at balancing …
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging
本文提出轨迹重置注意力控制TRAC与风格介质概念，构建免调优框架，在降低计算成本的同时实现图像和视频的高质量、内容保持的风格迁移。
Photorealistic style transfer is the task of synthesizing a realistic-looking image when adapting the content from one image to appear in the style of another image. Modern models commonly embed a transformation that fuses features describing the content image and style image and then decodes the resulting feature into a stylized image. We introduce a general-purpose transformation that enables controlling the balance between how much content is preserved and the strength of the infused style. We offer the first experiments that demonstrate the performance of existing transformations across di…
ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer
本文提出 ConsisLoRA，通过预测原始图像、两步解耦训练、逐步损失过渡和推理引导，提升 LoRA 风格迁移的内容与风格一致性并减少内容泄漏。
StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer
针对免训练扩散风格迁移中的内容布局改变与风格图内容泄漏，本文提出 StyleSSP，通过优化 DDIM 潜变量频率成分和反演负向引导改进采样起点，提升内容保持并减少泄漏。
Disentangling factors of variation within data has become a very challenging problem for image generation tasks. Current frameworks for training a Generative Adversarial Network (GAN), learn to disentangle the representations of the data in an unsupervised fashion and capture the most significant factors of the data variations. However, these approaches ignore the principle of content and style disentanglement in image generation, which means their learned latent code may alter the content and style of the generated images at the same time. This paper describes the Style and Content Disentangl…
UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement
UniCSG通过分阶段语义和频率解耦，解决风格迁移中内容与风格纠缠问题，提升生成内容的忠实度和稳定性。
TRACE: Time-Adaptive Residual Attention Control with Content-Style Decomposition for Training-Free Diffusion Style Transfer
提出免训练扩散风格迁移框架TRACE，通过内容-风格分解和时间自适应残差注意力控制，在提升风格保真度的同时减少内容泄漏，实验显示显著优于基线。
WikiStyle+: A Multimodal Approach to Content-Style Representation Disentanglement for Artistic Image Stylization
针对现有艺术图像风格化方法依赖图像监督导致的单模态限制和解耦不彻底问题，本文构建WikiStyle+多模态数据集，提出解耦表示引导的扩散模型，实现更精细且符合参考风格艺术特征的风格化。
StyleBlend: Enhancing Style-Specific Content Creation in Text-to-Image Diffusion Models
针对文本到图像扩散模型中难以同时对齐文本提示与特定艺术风格的问题，提出StyleBlend方法，通过分解风格为构图和纹理并采用双分支合成策略，实现文本对齐且风格一致的内容生成，解决了文本错位和风格弱表示问题。
InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer
提出 InfoStyler，通过信息瓶颈与跨域重构策略实现内容与风格特征的清晰解耦，有效平衡艺术风格迁移中的内容保持与风格丰富度。
Existing methods for AI-generated artworks still struggle with generating high-quality stylized content, where high-level semantics are preserved, or separating fine-grained styles from various artists. We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of variations when only one of them is labelled in general, and fully decompose complex anime illustrations into style and content in particular. Training such model is challenging, since given a style, various content data may exist but not the other way round. Our approach is divi…
TeleStyle: Content-Preserving Style Transfer in Images and Videos
TeleStyle通过课程持续学习框架实现图像和视频内容保持的风格迁移，提升风格相似性和内容一致性，达到最佳性能。
Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, …
InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
本文提出InstantStyle-Plus，通过解构风格迁移为风格、空间结构和语义内容三要素，结合内容潜在噪声初始化、tile ControlNet和全局语义适配器，在保持原图内容完整性的同时实现高效风格注入。
TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation
提出TeleStyle V2，通过自蒸馏数据合成和分布匹配蒸馏，支持四种内容-风格参考组合，解决内容一致性退化问题，性能与Qwen-Image-Edit和Gemini 3 Pro相当。
Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models
针对文本到图像扩散模型风格迁移中内容泄露问题，提出无需调参的图像特征元素掩盖方法，通过减少条件引导避免内容流入，经理论与多风格实验验证其有效性，提升风格迁移性能。
Implicit Style-Content Separation using B-LoRA
提出 B-LoRA 方法，利用 LoRA 联合训练 SDXL 中两个特定块以隐式分离图像风格与内容，显著提升风格化效果并缓解过拟合，支持多种风格化任务。
Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network
针对现有风格迁移方法计算成本高且易出现风格化不足或内容缺失的问题，提出仅含编码器的Puff-Net，通过纯内容与风格特征提取器实现高效且高质量的风格迁移。
Learning Dynamic Style Kernels for Artistic Style Transfer
本文提出学习动态风格核实现逐像素风格迁移，通过风格对齐编码和基于内容的门控调制模块兼顾全局与局部交互，在视觉质量和效率上优于现有方法。
LLM-Enabled Style and Content Regularization for Personalized Text-to-Image Generation
针对个性化文本到图像生成中风格化不足和内容不准确的问题，本文提出风格细化和内容保持策略，利用视觉推理提示优化风格嵌入并保持模型泛化能力，实验证明该方法生成效果更优。
AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer
针对现有风格迁移方法易破坏内容图像纹理的问题，提出AEANet，通过内容与风格亲和力增强注意力模块、混合注意力模块及局部不相似性损失，有效保留内容结构与艺术特征，在任意风格迁移中优于现有最先进方法。
StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance
本文提出StyleKeeper，通过扩展无分类器引导并引入负视觉查询引导（NVQG），在文本到图像生成中有效减少视觉提示的内容泄漏，确保风格迁移同时保持文本一致性。
One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input (style and content) images. An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called Deep…
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface. To generate a set of multi-content images following a consistent style from very few examples, we propose an end-to-end stacked conditional GAN model considering content along channels and style along network layers. Our proposed network transfers the style of given glyphs to the contents of unseen ones, capturing highly stylized fonts found in the real-world such as those on movie posters or infographics. We …
Arbitrary style transfer aims to synthesize a content image with the style of an image to create a third image that has never been seen before. Recent arbitrary style transfer algorithms find it challenging to balance the content structure and the style patterns. Moreover, simultaneously maintaining the global and local style patterns is difficult due to the patch-based mechanism. In this paper, we introduce a novel style-attentional network (SANet) that efficiently and flexibly integrates the local style patterns according to the semantic spatial distribution of the content image. A new ident…
Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization. However, the attention mechanism based on feature points ignores the feature multi-manifold distribution, where each feature manifold corresponds to a semantic region in the image. Consequently, a uniform content semantic region is rendered by highly different patterns from various style semantic regions, producing inconsistent stylization results with visual artifacts. We proposed the progress…
CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer
CLeaR提出无训练框架，通过正交子空间投影、集成反演和能量引导校准，解决风格迁移中内容泄漏与风格保真度的困境，在StyleBench上改善风格对齐并减少泄漏。
Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been shown effective for style transfer between two image categories. However, previous methods often assume a shared domain-invariant content space, which could compromise the content representation power. For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces. This way, images can be encoded more properly for style…
Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largely arises because labels do not consider the semantic distance. To mitigate such problems, we propose a style-aware discriminator that acts as a critic as well as a style encoder to provide conditions. The style-aware discriminator learns a controllable style space using prototype-based self-supervised learning and simultaneously guides the generator. Experiments on multiple dat…
Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the observation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by theory. To our knowledge, this is the first knowledge distillation method for photorealistic style transfer. Our experiments demonstrate its versatility for use with different backbone architectures, VGG and MobileNet, across six image resolutions. Compared…
Only-Style: Stylistic Consistency in Image Generation without Content Leakage
针对图像风格生成中内容泄露的问题，提出Only-Style方法，通过定位泄露并自适应调整风格对齐参数平衡风格一致性与泄露消除，还设计了新评估框架，性能优于现有方法。
Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been as…
SplitFlux: Learning to Decouple Content and Style from a Single Image
SplitFlux通过LoRA微调单流块，实现内容与风格的解耦，提升图像生成的质量和多样性。
In this work, we propose and validate a framework to leverage language-image pretraining representations for training-free zero-shot sketch-to-image synthesis. We show that disentangled content and style representations can be utilized to guide image generators to employ them as sketch-to-image generators without (re-)training any parameters. Our approach for disentangling style and content entails a simple method consisting of elementary arithmetic assuming compositionality of information in representations of input sketches. Our results demonstrate that this approach is competitive with stat…
Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer
通过系统探索层、时间步和ControlNet几何条件四个维度的调度，发现递减调度（浅层和早期时间步强结构注入）优于递增调度，且余弦和平方根时间步调度优于线性，结合近乎独立的gamma调度与ControlNet条件可扩展帕累托前沿，在ArtFID上相对提升6.1%。
AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models
AttenST提出免训练注意力驱动风格迁移框架，通过风格引导自注意力、风格保持反演、内容感知自适应实例归一化和双特征交叉注意力，在降低计算成本的同时实现结构保真与风格表达的最优融合。
Style transfer is a problem of rendering image with some content in the style of another image, for example a family photo in the style of a painting of some famous artist. The drawback of classical style transfer algorithm is that it imposes style uniformly on all parts of the content image, which perturbs central objects on the content image, such as faces or text, and makes them unrecognizable. This work proposes a novel style transfer algorithm which automatically detects central objects on the content image, generates spatial importance mask and imposes style non-uniformly: central object…
MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow
针对风格化动作生成中风格与内容单向控制易冲突的问题，提出MulSMo双向控制流并结合对比学习支持文本、图像等多模态风格输入，实验表明其在多数据集上显著优于现有方法。
Few-shot Image Generation via Style Adaptation and Content Preservation
针对少样本生成中微调GAN易过拟合、难以兼顾风格适应与内容多样性的问题，本文提出引入图像翻译模块的成对图像重建方法，使生成器与翻译模块互相促进，在少样本设置下稳定超越现有方法。
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout, and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the …
Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content code and domain-specific style code individually for multimodal purposes. However, less attention has been paid to interpreting and manipulating the translated image. In this paper, we propose to separate the content code and style code simultaneously in a unified framework. Based on the correlation between the latent features and the high-level domain-invariant tasks, the proposed framework demonstrates superior perf…
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to achieve good performance in spatially equivariant tasks (e.g. image-to-image translation). To achieve this, they employ different model design, learning objective, and data biases. While considerable effort has been made to measure disentanglement in vector representations, and assess its impact on task performance, such analysis for (spatial) content - style disentanglement is lacking. In this paper, we conduct an e…
Image style transfer aims to manipulate the appearance of a source image, or "content" image, to share similar texture and colors of a target "style" image. Ideally, the style transfer manipulation should also preserve the semantic content of the source image. A commonly used approach to assist in transferring styles is based on Gram matrix optimization. One problem of Gram matrix-based optimization is that it does not consider the correlation between colors and their styles. Specifically, certain textures or structures should be associated with specific colors. This is particularly challengin…
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
研究如何分离内容和风格表示，提出对比增强流匹配框架，将对比正则化融入可逆流匹配，在多个数据集实验中提升了内容和风格检索、增强嵌入簇分离及开集鲁棒性，改进了分布转移下的解缠和鲁棒性。
Despite having promising results, style transfer, which requires preparing style images in advance, may result in lack of creativity and accessibility. Following human instruction, on the other hand, is the most natural way to perform artistic style transfer that can significantly improve controllability for visual effect applications. We introduce a new task, language-driven artistic style transfer (LDAST), to manipulate the style of a content image, guided by a text. We propose contrastive language visual artist (CLVA) that learns to extract visual semantics from style instructions and accom…
AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer
研究图像引导风格迁移，指出基于扩散方法存在内容与风格解缠难题。提出AnyStyle框架，采用统一单适配器范式，结合无训练结构引导，提升可控性与稳定性，实验显示性能优。
Recent neural style transfer frameworks have obtained astonishing visual quality and flexibility in Single-style Transfer (SST), but little attention has been paid to Multi-style Transfer (MST) which refers to simultaneously transferring multiple styles to the same image. Compared to SST, MST has the potential to create more diverse and visually pleasing stylization results. In this paper, we propose the first MST framework to automatically incorporate multiple styles into one result based on regional semantics. We first improve the existing SST backbone network by introducing a novel multi-le…
Style-guided text image generation tries to synthesize text image by imitating reference image's appearance while keeping text content unaltered. The text image appearance includes many aspects. In this paper, we focus on transferring style image's background and foreground color patterns to the content image to generate photo-realistic text image. To achieve this goal, we propose 1) a content-style cross attention based pixel sampling approach to roughly mimicking the style text image's background; 2) a pixel-wise style modulation technique to transfer varying color patterns of the style imag…
SafeStyle: Calibrated Style Residual Injection for Controllable Style-Leakage Trade-off in Diffusion Stylization
SafeStyle提出免训练的校准风格残差注入方法，通过子空间估计和残差范数预算，在扩散风格化中平衡风格保真度与内容泄漏，实验显示高风格相似度且低语义泄漏。
Inversion-Free Style Transfer with Dual Rectified Flows
本文提出无反向过程的双修正流框架，通过并行预测和动态中点插值实现高效风格迁移，提升视觉保真度和计算效率。
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
针对风格迁移中预定义风格表示限制表达的问题，提出Z-STAR+方法，利用扩散模型潜在特征的自然分布，通过双去噪路径和交叉注意力重加权模块等，实现无需重训练的零样本风格迁移，提升效果。
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