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This paper presents the innovative design and successful deployment of a pioneering autonomous unmanned aerial system developed for executing the world's largest mural painted by a drone. Addressing the dual challenges of maintaining artistic precision and operational reliability under adverse outdoor conditions such as wind and direct sunlight, our work introduces a robust system capable of navigating and painting outdoors with unprecedented accuracy. Key to our approach is a novel navigation system that combines an infrared (IR) motion capture camera and LiDAR technology, enabling precise location tracking tailored specifically for largescale artistic applications. We employ a unique control architecture that uses different regulation in tangential and normal directions relative to the planned path, enabling precise trajectory tracking and stable line rendering. We also present algorithms for trajectory planning and path optimization, allowing for complex curve drawing and area filling. The system includes a custom-designed paint spraying mechanism, specifically engineered to function effectively amidst the turbulent airflow generated by the drone's propellers, which also protects the drone's critical components from paint-related damage, ensuring longevity and consistent performance. Experimental results demonstrate the system's robustness and precision in varied conditions, showcasing its potential for autonomous large-scale art creation and expanding the functional applications of robotics in creative fields.

Ancient murals are valuable cultural heritage with great archaeological value. They provide insights into ancient religions, ceremonies, folklore, among other things through their content. However, due to long-term oxidation and inadequate protection, ancient murals have suffered continuous damage, including peeling and mold etc. Additionally, since ancient murals were typically painted indoors, the light intensity in images captured by digital devices is often low. The poor visibility hampers the further restoration of damaged areas. To address the escalating damage to ancient murals and facilitate batch restoration at archaeological sites, we propose a two-stage restoration model with automatic defect area detection strategy which called MER(Mural Enhancement and Restoration net) for ancient murals that are damaged and have been captured in low light. Our two-stage model not only enhances the visual quality of restored images but also achieves commendable results in relevant metric evaluations compared with other competitors. Furthermore, we have launched a website dedicated to the restoration of ancient mural paintings, utilizing the proposed model. Code is available at https://gitee.com/bbfan2024/MER.git.

Abstract-- Robot art presents an opportunity to both showcase and advance state-of-the-art robotics through the challenging task of creating art. Creating large-scale artworks in particular engages the public in a way that small-scale works cannot, and the distinct qualities of brush strokes contribute to an organic and human-like quality. Combining the large scale of murals with the strokes of the brush medium presents an especially impactful result, but also introduces unique challenges in maintaining precise, dextrous motion control of the brush across such a large workspace. In this work, we present the first robot to our knowledge that can paint architectural-scale murals with a brush. We create a hybrid robot consisting of a cable-driven parallel robot and 4 degree of freedom (DoF) serial manipulator to paint a 27m by 3.7m mural on windows spanning 2-stories of a building. We discuss our approach to achieving both the scale and accuracy required for brush-painting a mural through a combination of novel mechanical design elements, coordinated planning and control, and on-site calibration algorithms with experimental validations.

Until now, AI systems have been largely designed to be algorithm-based and focused on input and output. But there is a conversation going on in both the digital technology and education sectors about the positive value of multidisciplinary design, and the importance of outcomes beyond revenue maximization and scaled efficiency. In 2024, creativity will drive innovation in AI. Until now AI has curated things, but the release of new generative tools offers enormous opportunities for creators. Guided by creative technologists and leaders on both sides of the creative-tech divide, AI in 2024 will be thoughtful, more inclusive, and impact-led.

A larger-than-life mural of Kobe Bryant and daughter Gianna will remain on the wall of Hardcore Fitness in downtown Los Angeles for at least another year. The video game company 2K Games, which features the late Lakers legend on the cover of "NBA 2K24," helped reach an agreement to save the beloved mural. Cecilia Moran, the gym's owner, told The Times in September that her landlord wanted the artwork removed by the end of the month so the space could be used for advertising. A change.org petition to save the mural received more than 90,000 signatures. Ronnie Singh, head of lifestyle and content marketing at 2K, announced his company's deal to help preserve the mural Monday on ESPN's "NBA Today."

The Yongle Palace murals, as valuable cultural heritage, have suffered varying degrees of damage, making their restoration of significant importance. However, the giant size and unique data of Yongle Palace murals present challenges for existing deep-learning based restoration methods: 1) The distinctive style introduces domain bias in traditional transfer learning-based restoration methods, while the scarcity of mural data further limits the applicability of these methods. 2) Additionally, the giant size of these murals results in a wider range of defect types and sizes, necessitating models with greater adaptability. Consequently, there is a lack of focus on deep learning-based restoration methods for the unique giant murals of Yongle Palace. Here, a 3M-Hybrid model is proposed to address these challenges. Firstly, based on the characteristic that the mural data frequency is prominent in the distribution of low and high frequency features, high and low frequency features are separately abstracted for complementary learning. Furthermore, we integrate a pre-trained Vision Transformer model (VIT) into the CNN module, allowing us to leverage the benefits of a large model while mitigating domain bias. Secondly, we mitigate seam and structural distortion issues resulting from the restoration of large defects by employing a multi-scale and multi-perspective strategy, including data segmentation and fusion. Experimental results demonstrate the efficacy of our proposed model. In regular-sized mural restoration, it improves SSIM and PSNR by 14.61% and 4.73%, respectively, compared to the best model among four representative CNN models. Additionally, it achieves favorable results in the final restoration of giant murals.

The gold-rush has started, and this time, it's not gold that's driving the frenzy -- it's AI. The world is currently witnessing an AI revolution that is changing the way we live, work, and interact. With advancements in machine learning, natural language processing, and computer vision, AI is becoming more intelligent and sophisticated, enabling it to take on complex tasks and decision-making processes. As AI technology continues to evolve, we also need to evolve in order to harness its full potential, avoid being obsolete and mitigate potential risks. ChatGPT is an AI language model developed by OpenAI that can generate human-like responses to text-based prompts.

Kotaku writes that the drawing of what some parents described as the face of Satan is actually a mask worn by the character Xiao in "Genshin Impact," a popular action role-playing game. In the game, Xiao is an immortal guardian of the Liyue region tasked with defeating the land's evil spirits. Donning the mask pictured in the mural triggers Xiao's ability Bane of All Evil, and its design is inspired by those worn in Nuo opera, a genre of Chinese opera developed from ancient religious ceremonies intended to drive away demons.

After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent's uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing video games, it is usually intractable in its most general form. In particular, deciding when and how to visit new states in the hopes of learning more about the environment can be challenging, especially when the reward signal is uninformative. These questions of reward specification and exploration are closely connected -- the more directed and "well shaped" a reward function is, the easier the problem of exploration becomes. The answer to the question of how to explore most effectively is likely to be closely informed by the particular choice of how we specify rewards.