arXiv subjects

Dekun Qian

Publications and source records attributed to Dekun Qian.

3 recordsLinked to original sources

arXiv2026

CompoVista: A Composition-Graph-Based Visual Analytics System for Compositional Analysis of Traditional Chinese Paintings

Compositional analysis of Traditional Chinese Paintings (TCPs) reveals how spatial arrangement, narrative structure, and cultural-aesthetic meaning are organized within the pictorial field. Traditional compositional analysis relies primarily on qualitative interpretation, supporting close examination of individual paintings but offering limited capacity to identify, compare, and validate compositional patterns across large-scale collections. To identify the key challenges in analyzing composition across large TCP collections, we collaborated with two art historians and conducted a complementary literature review. Drawing on the resulting insights, we introduce CompoGraph, a structured representation for composition-oriented analysis of TCPs. It represents the composition of a painting across four layers: entities, relations, voids, and context. Based on this representation, we develop CompoVista, a canvas-based visual analytics system for composition-oriented exploration of TCPs. CompoVista allows art historians to construct and refine painting cohorts through interactive compositional queries. It also supports inspecting entity distributions and relations at the cohort level, comparing compositional differences across cohorts, and tracing aggregate patterns back to painting-level evidence. Through two case studies, a user study, and expert interviews, we demonstrate that CompoVista can help art historians discover, compare, and validate compositional patterns across collections of TCPs.

cs.HC↗

arXiv2026

AniMaster: From Story Texts to Animated Videos via Cinematic Script Generation and Interactive Authoring

Recent advances in Video Generation Models (VGMs) have demonstrated strong capabilities in producing short video clips. However, it is still challenging for everyday creators to leverage these models to produce polished long-form animated videos from brief story texts. Informed by a formative study with both novice creators and film experts, we identify two major challenges of interactive video authoring: (1) the lack of expertise in translating free-form story texts to professional cinematic scripts and finally high-quality animated videos, and (2) the absence of effective ways to convey video design intents to key variables of visual storytelling, such as shot composition, camera controls and shot sequencing. Drawing on narratology and film studies, we propose a three-layer design framework that defines the key design dimensions across three layers (i.e., story texts, cinematic scripts, and animated videos) as well as the translation between them. Built on this framework, we present AniMaster, a VGM-powered authoring tool to enable everyday creators to easily produce smooth animated videos from free-form story texts. AniMaster automatically expands brief story texts to detailed cinematic scripts, and further translates cinematic scripts into polished videos by following professional visual storytelling principles. It also allows users to interactively edit the scripts and refine the generated videos via text instructions and intuitive interactions. We extensively evaluated AniMaster through an in-depth user study with 16 participants, two case studies, and expert interviews with 2 film professionals. The results demonstrate the effectiveness and usability of AniMaster in helping everyday creators create polished animated videos from free-form story texts.

cs.HC↗

arXiv2026

VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting

Structured representation can characterize semantic objects and relationships in images. It provides a possible effective way for the semantic understanding of Traditional Chinese Paintings (TCPs) to better support archaeology and art history research. However, most image-oriented structured representation methods perform poorly on TCPs, due to two major challenges: 1) the objects and events of TCPs exhibit substantial differences from modern natural images, which results in semantic misunderstandings of TCPs; and 2) it is difficult to achieve accurate identification of ancient objects and events in TCPs, even for domain experts.In this paper, we propose VisTCP, a visualization framework that combines a TCP-oriented intelligent model and expert knowledge, which enables art historians to achieve trustworthy structured representations of TCPs in a human-in-the-loop manner. Firstly, we conduct a pilot study with three domain experts to build a semantic taxonomy of TCPs. Then, expert-annotated data are used to train a TCP-oriented structured representation model, which can automatically extract meaningful objects and their relationships in TCPs. To inform users of the model uncertainty, we design a joint embedding visualization view to show the differences between expert annotations and model predictions. This allows users to refine the structured representation based on their domain knowledge, enabling iterative optimization of the model. Finally, we conduct a case study, a usage scenario, and expert interviews on a real dataset to demonstrate the effectiveness of VisTCP in supporting the structured representation and semantic understanding of TCPs.

cs.HC↗