[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-455632-105":59,"doc-detail-455632-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","automatic-background-animation-generation-aligned-with-llm-generated-lyrics-for-childrens-songs","Automatic background animation generation aligned with LLM-generated lyrics for children’s songs","","Media content creation for children’s songs is time-consuming and costly, motivating automated approaches. This paper proposes BAGen, a generative pipeline that creates background animations by generating lyrics with a language model, producing background images using a diffusion model, and overlaying dynamic visual effects for enhanced alignment. Experiments compare conventional diffusion and prompt-engineering strategies, highlighting CascadeSD and effective landscape or image-style prompting. The study also benchmarks text-to-video models and reports quantitative evaluations of produced visuals.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automatic-background-animation-generation-aligned-with-llm-generated-lyrics-for-childrens-songs/455632/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automatic-background-animation-generation-aligned-with-llm-generated-lyrics-for-childrens-songs/455632.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What is the BAGen pipeline designed to generate?","Question",{"text":111,"@type":112},"BAGen generates background animations for children’s songs by combining lyrical understanding, image synthesis, and dynamic visual effects that enhance the final output.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How does BAGen align visuals with the lyrics?",{"text":116,"@type":112},"BAGen uses a prompt-engineering framework to connect lyrical and emotional cues from language-model-generated lyrics to diffusion-based visual synthesis, supporting semantic coherence and safety constraints.",{"name":118,"@type":109,"acceptedAnswer":119},"What did the experiments show about diffusion models and prompting?",{"text":120,"@type":112},"Under identical conditions, the study systematically compares multiple diffusion models and identifies CascadeSD as the most effective, with landscape or image-style prompting improving lyric-driven image generation.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},455632,1790743672,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":41},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAutomatic background animation generation aligned with LLMgenerated lyrics for children’s  \nsongs  \nSanghyuck Lee1, Timur Khairulov1, Ye-Chan Park1, Wangduk Seo2,4􀀍 & Jaesung Lee1,3,4􀀍  \nMedia content creation is a labor-intensive and expensive process requiring significant time. Recent developments in artificial intelligence have introduced generative models, which have significant potential in the entertainment industry. Meanwhile, demand for video content tailored to children’s songs has steadily increased, reflecting their significant contribution to early education and entertainment. In this paper, we present a generative model-based approach to automated video creation for children’s songs. The proposed pipeline consists of three key steps: generating lyrics using a language model, producing background images with a diffusion model, and overlaying dynamic visual effects to enhance the final output. Our experiments include a comparison of conventional diffusion models and prompt engineering methods, highlighting the superior performance of CascadeSD and the efficacy of landscape or image-style prompting. Lastly, we provide experimental results comparing text-to-video models with our pipeline. The code for our project is available in the following repository: [https://github.com/KhrTim/BAGen](https://github.com/KhrTim/BAGen).  \nChildren’s songs have long been used as an effective medium for nurturing children’s future behavior, social integration, and emotional development1. Throughout the history of human society, early childhood education has been a significant factor influencing the personality formation of children. Specifically, children’s songs are both educational and entertaining, forming a traditional method for supporting early learning2. With societal evolution, lifestyle changes, and technological advancements, conventional educational methods have adapted to modern conditions by incorporating media technologies into early childhood education3. In addition, the rapid development of generative models has elevated the entertainment industry, creating new opportunities for innovative educational and entertainment content4.  \nMedia content creation has traditionally relied on the collaborative efforts of multidisciplinary teams, each contributing unique expertise to the process5. Furthermore, this process typically requires significant time and money6. These constraints and requirements push businesses to seek and adopt novel methods that preserve the quality of work while reducing the cost and creation time. Hence, there is a growing demand for practical generation tools that can automate and enhance the workflows of media creation teams7. Furthermore, recent advancements in generative models show their ability to produce high-quality, visually appealing images8. Generative models are revolutionizing digital media production by streamlining routine tasks, enabling creators to focus on the more creative and essential aspects of media creation. Such changes are also noticeable in content creation companies targeting children audiences9.  \nCreating background animations for children’s songs is a complex process, which involves interpretingll architecture of the proposed method. unique linguistic and emotional elements. Translating these elements into effective prompts for generative models is particularly challenging10. Moreover, existing text-to-video and animation systems are primarily designed for general-purpose media and often lack the controllability, semantic alignment, and safety constraints required for educational or child-oriented content11, 12. They typically operate as monolithic black-box models that map text directly to video, making it difficult to adapt them for domainspecific storytelling, mood alignment, or age-appropriate visuals13. To address these challenges, we propose a modular and extensible pipeline–BAGen–that ex","cbCaidnEkB5pH1qd","https://ap.wps.com/l/cbCaidnEkB5pH1qd","pdf",5347047,12,"English","# Introduction\n## Motivation and background\n## Problem with existing systems\n# Proposed Pipeline (BAGen)\n## Step 1: Lyrics generation with LLM\n## Step 2: Background images with diffusion\n## Step 3: Dynamic visual effect integration\n# Experimental Setup and Results\n## Comparison of diffusion models and prompting methods\n## Benchmarking text-to-video models","[{\"question\":\"What is the BAGen pipeline designed to generate?\",\"answer\":\"BAGen generates background animations for children’s songs by combining lyrical understanding, image synthesis, and dynamic visual effects that enhance the final output.\"},{\"question\":\"How does BAGen align visuals with the lyrics?\",\"answer\":\"BAGen uses a prompt-engineering framework to connect lyrical and emotional cues from language-model-generated lyrics to diffusion-based visual synthesis, supporting semantic coherence and safety constraints.\"},{\"question\":\"What did the experiments show about diffusion models and prompting?\",\"answer\":\"Under identical conditions, the study systematically compares multiple diffusion models and identifies CascadeSD as the most effective, with landscape or image-style prompting improving lyric-driven image generation.\"}]","Automatic background animation generation aligned with LLM-generated lyrics for children’s songs | PDF"]