Creating and understanding art has long been a hallmark of human ability. When presented with finished digital artwork, professional graphic artists can intuitively deconstruct and replicate it using various drawing tools, such as the line tool, paint bucket, and layer features, including opacity and blending modes. While most recent research in this field has focused on art generation, proposing a range of methods, these often rely on the concept of artwork being represented as a final image. To bridge the gap between pixel-level results and the actual drawing process, we present an approach that treats a set of drawing tools as executable programs. This method predicts a sequence of steps to achieve the final image, allowing for understandable and resolution-independent reproductions under the usage of a set of drawing commands. Our experiments demonstrate that our program synthesizer, Art2Prog, can comprehensively understand complex input images and reproduce them using high-quality executable programs. The experimental results evidence the potential of machines to grasp higher-level information from images and generate compact program-level descriptions.
Art2Mus: Artwork-to-Music Generation via Visual Conditioning and Large-Scale Cross-Modal Alignment
University of Bari Aldo Moro（巴里大学阿尔多·莫罗大学）；Catalonia's Telecommunications Technology Centre（加泰罗尼亚电信技术中心）；University of Picardie Jules Verne（皮卡第大学朱利·瓦内大学）
CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Pengcheng Laboratory（鹏城实验室）；MAIS, Institute of Automation, Chinese Academy of Sciences（自动化所MAIS部）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；ByteDance Inc.（字节跳动公司）；National Cheng Kung University（国立成功大学）
CE-NPBG: Connectivity Enhanced Neural Point-Based Graphics for Novel View Synthesis in Autonomous Driving Scenes
Computer Vision Center (CVC), Universitat Autònoma de Barcelona（计算机视觉中心（CVC），巴塞罗那自治大学）；Intelligent Simulation Innovation Lab, Huawei Munich Research Center（智能仿真创新实验室，华为慕尼黑研究中心）
DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
NVIDIA（英伟达）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Waterloo（滑铁卢大学）；Stanford University（斯坦福大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
L’Oréal Research and Innovation（欧莱雅研究与创新部）；Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院电信巴黎）；Modiface；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）
