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Chinese ink painters of the Republican period (1911–1949) creatively engaged with a range of art forms in addition to ink, such as oil painting, drawing, photography, and woodblock prints. They transformed their medium of choice in innovative ways, reinterpreting both its history and its theoretical foundations. Juliane Noth offers a new understanding of these compelling experiments in Chinese painting by studying them as transmedial practice, at once shaped by and integral to the modern global art world. Transmedial Landscapes and Modern Chinese Painting shines a spotlight on the mid-1930s, a period of intense productivity in which Chinese artists created an enormous number of artworks and theoretical texts. The book focuses on the works of three seminal artists, Huang Binhong, He Tianjian, and Yu Jianhua, facilitating fresh insights into this formative stage of their careers and into their collaborations in artworks and publications. In a nuanced reading of paintings, photographs, and literary and theoretical texts, Noth shows how artworks and discussions about the future of ink painting were intimately linked to the reshaping of the country through infrastructure development and tourism, thus leading to the creation of a uniquely modern Chinese landscape imagery.

This is a provocative essay of reflections on traditional mainstream scholarship on Chinese art as done by towering figures in the field such as James Cahill and Wen Fong. James Elkins offers an engaging and accessible survey of his personal journey encountering and interpreting Chinese art through Western scholars' writings. He argues that the search for optimal comparisons is itself a modern, Western interest, and that art history as a discipline is inherently Western in several identifiable senses. Although he concentrates on art history in this book, and on Chinese painting in particular, these issues bear implications for Sinology in general, and for wider questions about humanistic inquiry and historical writing. Jennifer Purtle's Foreword provides a useful counterpoint from the perspective of a Chinese art specialist, anticipating and responding to other specialists’ likely reactions to Elkins's hypotheses.

The artistic expression inherent in Chinese paintings serves as a conduit for the artists’ emotional and cognitive expression. However, current research lacks consensus regarding the distinct psychological mechanisms underlying the appreciation of Chinese painting genres (landscape vs. figure paintings). This study—employing a vocabulary generation task and the Implicit Association Test (IAT) to compare semantic representation and emotional awareness during participants’ viewing these two types of paintings—aims to elucidate potential disparities in aesthetic processing. In Experiment 1, although both types of paintings produced an abundance of content words, figure paintings elicited a greater number of emotional association words than landscape paintings. Meanwhile, Experiment 2 demonstrated faster response times for an incompatible joint task versus a compatible joint task. These findings collectively suggest that the aesthetic of paintings may engage automatic processes, with the effects on semantic representation and emotional awareness appearing to be independent of the type of paintings. The predominance of content processing over emotional response may be attributed to the temporal characteristics of emotional arousal.

The automatic and precise perceptual parsing of Chinese landscape paintings (CLP) significantly aids in the digitization and recreation of artworks. Manual extraction and analysis of objects in CLPs is challenging, even for expert painters with professional knowledge and sharp discernment. Two main key reasons restricted the development of CLP parsing: (1) a lack of pixel-level labeled data used to supervise model training, and (2) the inherent complexity of CLP images compared to real scenes, characterized by varied scales, diverse textures, and intricate empty spaces. To address these challenges, we first construct a pixel-level annotated CLP segmentation datasets to advance perceptual parsing. Then, a novel CLP Perceptual Parsing (CLPPP) model is designed to fully utilize the intrinsic features of CLP images. To dynamically and adaptively capture context information, we introduced a set of learnable kernels into the CLPPP model based on the multiscale features of objects within CLPs. This enabled the model to learn an appropriate receptive field for context information extraction. Additionally, a positional attention head is devised to effectively eliminate noise from the intergroup and help the kernel gain inter-object position information. This iterative optimization process is helpful to learn powerful feature representations for different textures in CLPs. The experiment results demonstrate that the proposed CLPPP model outperforms state-of-the-art methods with mIoU, aAcc, and mAcc scores of 55.45, 75.08, and 71.15, respectively, achieving a large margin on the CLP dataset under consistent conditions.

Chinese landscape painting has a unique and artistic style, and its drawing technique is highly abstract in both the use of color and the realistic representation of objects. Previous methods focus on transferring from modern photos to ancient ink paintings. However, little attention has been paid to translating landscape paintings into modern photos. To solve such problems, in this paper, we (1) propose DLP-GAN (Draw Modern Chinese Landscape Photos with Generative Adversarial Network), an unsupervised cross-domain image translation framework with a novel asymmetric cycle mapping, and (2) introduce a generator based on a dense-fusion module to match different translation directions. Moreover, a dual-consistency loss is proposed to balance the realism and abstraction of model painting. In this way, our model can draw landscape photos and sketches in the modern sense. Finally, based on our collection of modern landscape and sketch datasets, we compare the images generated by our model with other benchmarks. Extensive experiments including user studies show that our model outperforms state-of-the-art methods.

As one of the precious cultural heritages, Chinese landscape painting has developed unique styles and techniques. Researching the intelligent generation of Chinese landscape paintings from photos can benefit the inheritance of traditional Chinese culture. To address detail loss, blurred outlines, and poor style transfer in present generated results, a model for generating Chinese landscape paintings from photos named Paint-CUT is proposed. In order to solve the problem of detail loss, the SA-ResBlock module is proposed by combining shuffle attention with the resblocks in the generator, which is used to enhance the generator’s ability to extract the main scene information and texture features. In order to solve the problem of poor style transfer, perceptual loss is introduced to constrain the model in terms of content and style. The pre-trained VGG is used to extract the content and style features to calculate the perceptual loss and, then, the loss can guide the model to generate landscape paintings with similar content to landscape photos and a similar style to target landscape paintings. In order to solve the problem of blurred outlines in generated landscape paintings, edge loss is proposed to the model. The Canny edge detection is used to generate edge maps and, then, the edge loss between edge maps of landscape photos and generated landscape paintings is calculated. The generated landscape paintings have clear outlines and details by adding edge loss. Comparison experiments and ablation experiments are performed on the proposed model. Experiments show that the proposed model can generate Chinese landscape paintings with clear outlines, rich details, and realistic style. Generated paintings not only retain the details of landscape photos, such as texture and outlines of mountains, but also have similar styles to the target paintings, such as colors and brush strokes. So, the generation quality of Chinese landscape paintings has improved.

Ancient paintings, as invaluable cultural heritage, often suffer from damages like creases, mold, and missing regions. Current restoration methods, while effective for natural images, struggle with the fine-grained control required for ancient paintings’ artistic styles and brushstroke patterns. We propose the Semantic and Sketch-Guided Restoration (SSGR) framework, which uses pixel-level semantic maps to restore missing and mold-affected areas and depth-aware sketch maps to ensure texture continuity in creased regions. The sketch maps are automatically extracted using advanced methods that preserve original brushstroke styles while conveying geometry and semantics. SSGR employs a semantic segmentation network to categorize painting regions and depth-sensitive sketch extraction to guide a diffusion model. To enhance style controllability, we cluster diverse attributes of landscape paintings and incorporate a Semantic-Sketch-Attribute-Normalization (SSAN) block that explores consistent patterns across styles through spatial semantic and attribute-adaptive normalization modules. Evaluated on the CLP-2K dataset, SSGR achieves an mIoU of 53.30%, SSIM of 0.42, and PSNR of 13.11, outperforming state-of-the-art methods. This approach not only preserves historical aesthetics but also advances digital heritage preservation with a tailored, controllable technique for ancient paintings.

Traditional Chinese landscape painting is prone to low-resolution image issues during the digital protection process. To reconstruct high-quality images from low-resolution landscape paintings, we propose a novel Chinese landscape painting generation diffusion probabilistic model (CLDiff), which is similar to the Langevin dynamic process, and realizes the transformation of the Gaussian distribution into the empirical data distribution through multiple iterative refinement steps. The proposed CLDiff can provide ink texture clear super-resolution predictions by gradually transforming the pure Gaussian noise into a super-resolution landscape painting condition on a low-resolution input through a parameterized Markov Chain. Moreover, by introducing an attention module with an energy function into the U-Net architecture, we turn the denoising diffusion probabilistic model into a powerful generator. Experimental results show that CLDiff achieves better visual results and highly competitive performance in traditional Chinese Landscape painting super-resolution tasks.

This study examines the intriguing peculiarities of the surface paint layer found in the painting Village scene (1931) by renowned Singapore artist Liu Kang (1911–2004). The incorporation of non-invasive visible light (VIS) and near-infrared (NIR) photography techniques, combined with high-power digital microscopy, revealed unusual features on the surface paint layer. Flattened impastos, clusters of incrusted foreign paint unrelated to the existing paint scheme, and fragments of paper with printed traditional Chinese characters were identified on the painting’s surface. The results of the analyses cross-referenced with the archival photographs enabled the consideration of the specified features of the paint layer as unintentional damage caused by the artist due to inadequate storage and transportation conditions—paradoxically, in his attempt to protect the painting. As these damaged areas pose potential display and conservation problems, three conservation strategies were proposed based on ethical guidelines formulated by various governing bodies for the conservation profession. This study demonstrates that there is no universal conservation solution that can satisfy conflicting aesthetic and ethical opinions. The damage to the paint layer affects the visual properties of the artwork but also provides evidence of its complex history. In light of the above, there may be valid arguments both for returning the painting to its original state and for preserving its current condition. Therefore, good practice would require balanced judgments from conservators and curators, considering Village scene in the broader context of Liu Kang’s early painting practice and the existing archival information about the artist.

How to migrate text-to-image models based on pre-trained diffusion models to adapt them to domain generation tasks is a common problem. In particular, the generation task for Chinese landscape paintings with unique characteristics suffers from a scarcity of fine-grained contextual details specific to such artwork. Moreover, the use of substantial amounts of non-landscape painting data during pre-training predisposes the model to be swayed by alternative visual styles, thereby leading to generated images that inadvertently lack the distinctive traits inherent to Chinese paintings. In this paper, we propose a Fine-grained Hierarchical Semantic Adapter for Chinese landscape paintings generation, namely FHS-adapter. The method orchestrates the diffusion process in a batch-wise manner, leveraging external fine-grained multi-perspective information to guide it. It gradually diminishes the influence of other style images embedded in the pre-trained diffusion model, ultimately preserving a greater number of landscape painting elements. The encoder was also replaced with the Taiyi-CLIP encoder, which is adapted for Chinese. We propose T2ICLP, a multimodal dataset containing 10,000 high-quality image-text pairs of Chinese landscape paintings. Unlike previous datasets, this dataset extracts fine-grained textual information from four perspectives, including Meta, Description, Sentiment, Poem. We compared the proposed model with the mainstream diffusion-based T2I models. Through an anonymous user study, our FHS-adapter method performs well in simulating various aspects such as brushwork, e.g.‘Gou, Cun, Dian, Ran’ means hooking, texturing, dotting, and dyeing, compositional space, elemental proportions, and color usage of different painting genres and artists. Our dataset is available at https://github.com/T2ICLP/t2iclp.