This paper presents a real-time generative drawing system that interprets and integrates both formal intent - the structural, compositional, and stylistic attributes of a sketch - and contextual intent - the semantic and thematic meaning inferred from its visual content - into a unified transformation process. Unlike conventional text-prompt-based generative systems, which primarily capture high-level contextual descriptions, our approach simultaneously analyzes ground-level intuitive geometric features such as line trajectories, proportions, and spatial arrangement, and high-level semantic cues extracted via vision-language models. These dual intent signals are jointly conditioned in a multi-stage generation pipeline that combines contour-preserving structural control with style- and content-aware image synthesis. Implemented with a touchscreen-based interface and distributed inference architecture, the system achieves low-latency, two-stage transformation while supporting multi-user collaboration on shared canvases. The resulting platform enables participants, regardless of artistic expertise, to engage in synchronous, co-authored visual creation, redefining human-AI interaction as a process of co-creation and mutual enhancement.
Pencils to Pixels: A Systematic Study of Creative Drawings across Children, Adults and AI
Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）；Max Planck School of Cognition（马克斯·普朗克认知学院）；University of Tübingen（蒂宾根大学）；University of Amsterdam（阿姆斯特丹大学）
Probing and Inducing Combinational Creativity in Vision-Language Models
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Yuanpei College, Peking University（北京大学元培学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（人工智能通用基础理论国家重点实验室）；Center on Frontiers of Computing Studies, School of Computer Science, Peking University（计算机科学学院前沿计算研究中心）
Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings
Department of Computer Science, South China Normal University, Guang Zhou, China（华南师范大学计算机学院）；Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China（香港城市大学计算机学院）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong（香港中文大学）；Bytedance（字节跳动）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）
Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation
Singapore Institute of Manufacturing Technology (SIMTech), A*STAR, Singapore（新加坡制造技术研究所）；Advanced Remanufacturing and Technology Centre (ARTC), A*STAR, Singapore（先进再制造与技术中心）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院）
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining（北京市交通数据分析与挖掘重点实验室）；Department of Computer Science, Aalborg University（奥尔堡大学计算机系）
We present an investigation into how representational losses can affect the drawings produced by artificial agents playing a communication game. Building upon recent advances, we show that a combination of powerful pretrained encoder networks, with appropriate inductive biases, can lead to agents that draw recognisable sketches, whilst still communicating well. Further, we start to develop an approach to help automatically analyse the semantic content being conveyed by a sketch and demonstrate that current approaches to inducing perceptual biases lead to a notion of objectness being a key feat…
DrawMotion: Generating 3D Human Motions by Freehand Drawing
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Science and Technology of China（中国科学技术大学）；NLP Lab, School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院自然语言处理实验室）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；The Institute of AI (TeleAI), China Telecom（中国电信人工智能研究院）；Northwestern Polytechnical University（西北工业大学）
Institute of Digital Games University of Malta（马耳他大学数字游戏研究所）
This paper introduces a novel method for generating artistic images that express particular affective states. Leveraging state-of-the-art deep learning methods for visual generation (through generative adversarial networks), semantic models from OpenAI, and the annotated dataset of the visual art encyclopedia WikiArt, our AffectGAN model is able to generate images based on specific or broad semantic prompts and intended affective outcomes. A small dataset of 32 images generated by AffectGAN is annotated by 50 participants in terms of the particular emotion they elicit, as well as their quality…
