Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds and Stanford Cars-196 datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI dataset. Source code and models are available at https://github.com/1jsingh/semantic-guidance.
GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance
Beihang University（北京航空航天大学）；The Chinese University of Hong Kong（香港中文大学）；Chinese Academy of Sciences（中国科学院）；Centre for Perceptual and Interactive Intelligence（感知与交互智能研究中心）
RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance
College of Artificial Intelligence, Nankai University, Tianjin, China（人工智能学院，南开大学，天津，中国）；School of Data Science, Fudan University, Shanghai, China（数据科学学院，复旦大学，上海，中国）
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）
Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance
National University of Singapore（新加坡国立大学）；Guangzhou University（广州大学）；Tsinghua University（清华大学）；South China University of Technology（华南理工大学）
CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation
National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；Alibaba Group（阿里巴巴集团）
Multi-Perspective Subimage CLIP with Keyword Guidance for Remote Sensing Image-Text Retrieval
School of Computer Technology and Applications（计算机技术与应用学院）；Qinghai University（青海大学）；Qinghai Provincial Laboratory for Intelligent Computing and Application（青海省智能计算与应用实验室）
