Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.

The amusement as well as media (E&M) business is actually a diverse sector composed of several segments such as media, television, and film streamed online. By 2021, the U.S. E&M business is projected to reach $759 billion in revenue, increasing at a compound annual growth rate (CAGR) of 3.6 percent. Despite the anticipated growth, there are concerns about a revenue declines in more traditional market segments. Being a result, business analysts like PwC argue that user experience need to take going up priority and AI is actually among top emerging technologies poised to positively add to our energy. Within this document we break down uses of artificial intelligence of the entertainment as well as media business market to offer company leaders with an understanding of present and emerging trends that could influence the sector of theirs.

The entertainment and media (E&M) industry is a diverse sector composed of multiple segments including film, television and media streamed online. By 2021, the U.S. E&M industry is projected to reach $759 billion in revenue, increasing at a compound annual growth rate (CAGR) of 3.6 percent. Despite the anticipated growth, there are concerns about a revenue declines in more traditional market segments. As a result, industry analysts such as PwC argue that user experience must take increasing priority and AI is among leading emerging technologies poised to positively contribute to this effort. In this article we break down applications of artificial intelligence in the entertainment and media industry market to provide business leaders with an understanding of current and emerging trends that may impact their sector. We'll begin with a synopsis of the sectors we covered: In the full article below, we'll explore the AI applications of each application by section and provide representative examples.

We've known for some time we have a content problem. Research from Smart Insights shows that more than 3.8 million pieces of online content are posted every 60 seconds. We are producing and sharing an unprecedented amount of content that we are struggling to keep track of. The Content Marketing Institute says many marketers don't know what content they have, where to find it, and how to leverage it. Without a doubt, we are drowning in content.

Artificial intelligence in your digital asset management (DAM) portal does the heavy lifting of tagging images for you. A retailer frequently uploading hundreds of visual assets, for example, won't need to manually add general image information and keywords. Allow everyone to find what they need in no time. Content that is not tagged, can be difficult or impossible to find quickly. With automated tags generated with 80% accuracy upon upload, not only marketers - but entire businesses - will they find what they need faster.