Normalized Coordinate Code, to release the model creativity at all possible.

We present a node-based storytelling system for multimodal content generation. The system represents stories as graphs of nodes that can be expanded, edited, and iteratively refined through direct user edits and natural-language prompts. Each node can integrate text, images, audio, and video, allowing creators to compose multimodal narratives. A task selection agent routes between specialized generative tasks that handle story generation, node structure reasoning, node diagram formatting, and context generation. The interface supports targeted editing of individual nodes, automatic branching for parallel storylines, and node-based iterative refinement. Our results demonstrate that node-based editing supports control over narrative structure and iterative generation of text, images, audio, and video. We report quantitative outcomes on automatic story outline generation and qualitative observations of editing workflows. Finally, we discuss current limitations such as scalability to longer narratives and consistency across multiple nodes, and outline future work toward human-in-the-loop and user-centered creative AI tools.

Normalized Coordinate Code, to release the model creativity at all possible.

Artificial Intelligence (AI) is increasingly permeating classrooms worldwide, yet its adoption in schools of developing countries remains under-explored. This paper investigates AI adoption, usage patterns, and perceived impact in Pakistani K-12 schools based on a survey of 125 educators. The questionnaire covered educator's familiarity with AI, frequency and modes of use, and attitudes toward AI's benefits and challenges. Results reveal a generally positive disposition towards AI: over two-thirds of teachers expressed willingness to adopt AI tools given proper support and many have begun integrating AI for lesson planning and content creation. However, AI usage is uneven - while about one-third of respondents actively use AI tools frequently, others remain occasional users. Content generation emerged as the most common AI application, whereas AI-driven grading and feedback are rarely used. Teachers reported moderate improvements in student engagement and efficiency due to AI, but also voiced concerns about equitable access. These findings highlight both the enthusiasm for AI's potential in Pakistan's schools and the need for training and infrastructure to ensure inclusive and effective implementation.

High-quality textures are critical for realistic 3D content creation, yet existing generative methods are slow, rely on UV maps, and often fail to remain faithful to a reference image. To address these challenges, we propose a transformer-based framework that predicts a 3D texture field directly from a single image and a mesh, eliminating the need for UV mapping and differentiable rendering, and enabling faster texture generation. Our method integrates a triplane representation with depth-based backprojection losses, enabling efficient training and faster inference. Once trained, it generates high-fidelity textures in a single forward pass, requiring only 0.2s per shape. Extensive qualitative, quantitative, and user preference evaluations demonstrate that our method outperforms state-of-the-art baselines on single-image texture reconstruction in terms of both fidelity to the input image and perceptual quality, highlighting its practicality for scalable, high-quality, and controllable 3D content creation.

With the rapid advancement of artificial intelligence (AI), the proliferation of AI-generated content (AIGC) tasks has significantly accelerated developments in text-to-video generation. As a result, the field of video production is undergoing a transformative shift. However, conventional text-to-video models are typically constrained by high computational costs. In this study, we propose Video-Generation-Team (VGTeam), a novel slide show video generation system designed to redefine the video creation pipeline through the integration of large language models (LLMs). VGTeam is composed of a suite of communicative agents, each responsible for a distinct aspect of video generation, such as scriptwriting, scene creation, and audio design. These agents operate collaboratively within a chat tower workflow, transforming user-provided textual prompts into coherent, slide-style narrative videos. By emulating the sequential stages of traditional video production, VGTeam achieves remarkable improvements in both efficiency and scalability, while substantially reducing computational overhead. On average, the system generates videos at a cost of only $0.103, with a successful generation rate of 98.4%. Importantly, this framework maintains a high degree of creative fidelity and customization. The implications of VGTeam are far-reaching. It democratizes video production by enabling broader access to high-quality content creation without the need for extensive resources. Furthermore, it highlights the transformative potential of language models in creative domains and positions VGTeam as a pioneering system for next-generation content creation.

Have you always fantasized about becoming a content creator? Whether you're looking to highlight your gym gains or bring college essays on the Roman Empire to life, content creation is more than just hitting record. It requires professional audio, cool shots, and fun effects to keep an audience captive. Fortunately, you can save hours on video tutorials and skip to the fun part with the help of a lifetime subscription to Canvid's AI-Powered Video Creator and Editor, now on sale for 59.99 (reg. Now, I know I just said content creation is more than just hitting record, but with Canvid, it can be.

As e-commerce rapidly integrates artificial intelligence for content creation and product recommendations, these technologies offer significant benefits in personalization and efficiency. AI-driven systems automate product descriptions, generate dynamic advertisements, and deliver tailored recommendations based on consumer behavior, as seen in major platforms like Amazon and Shopify. However, the widespread use of AI in e-commerce raises crucial ethical challenges, particularly around data privacy, algorithmic bias, and consumer autonomy. Bias -- whether cultural, gender-based, or socioeconomic -- can be inadvertently embedded in AI models, leading to inequitable product recommendations and reinforcing harmful stereotypes. This paper examines the ethical implications of AI-driven content creation and product recommendations, emphasizing the need for frameworks to ensure fairness, transparency, and need for more established and robust ethical standards. We propose actionable best practices to remove bias and ensure inclusivity, such as conducting regular audits of algorithms, diversifying training data, and incorporating fairness metrics into AI models. Additionally, we discuss frameworks for ethical conformance that focus on safeguarding consumer data privacy, promoting transparency in decision-making processes, and enhancing consumer autonomy. By addressing these issues, we provide guidelines for responsibly utilizing AI in e-commerce applications for content creation and product recommendations, ensuring that these technologies are both effective and ethically sound.

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep learning methods for reflection removal and 3D reconstruction. By leveraging state-of-the-art generative models like Stable Diffusion, the methodology translates natural language inputs into detailed 3D models through a multi-stage workflow. The reconstruction process begins with the generation of high-quality images from textual prompts, followed by enhancement by a reinforcement learning agent and reflection removal using the Stable Delight model. Advanced image upscaling and background removal techniques are then applied to further enhance visual fidelity. These refined two-dimensional representations are subsequently transformed into volumetric 3D models using sophisticated machine learning algorithms, capturing intricate spatial relationships and geometric characteristics. This process achieves a highly structured and detailed output, ensuring that the final 3D models reflect both semantic accuracy and geometric precision. This approach addresses key challenges in generative reconstruction, such as maintaining semantic coherence, managing geometric complexity, and preserving detailed visual information. Comprehensive experimental evaluations will assess reconstruction quality, semantic accuracy, and geometric fidelity across diverse domains and varying levels of complexity. By demonstrating the potential of AI-driven 3D reconstruction techniques, this research offers significant implications for fields such as augmented reality (AR), virtual reality (VR), and digital content creation.

Just like streaming services, juggling multiple subscriptions for different AI tools is overwhelming and costly, but this revolutionary platform will break you free from monthly fees. You gain lifetime access to a suite of powerful features that can streamline your creative process for just 40--less than three months of a single ChatGPT subscription. The idea behind 1minAI is simple yet groundbreaking. Instead of juggling separate accounts and subscriptions, I now enjoy the benefits of top-tier AI tools like ChatGPT, Gemini, and Midjourney, all organized neatly in one dashboard. This integration allows me to generate text, create images, and refine content seamlessly, saving time and money.