Cesar Romero Pose’s Post

Text-to-Image Generation: Models like DALL-E, Stable Diffusion, and Midjourney can create realistic images from textual descriptions, revolutionizing fields like digital art, product design, and marketing. #GenerativeAI  #ArtificialIntelligence  #DALLE  #MidJourney  #GPT3 

In case anyone let this one sneak past their inbox just FYI... this is a phenomenal improvement (inevitable some would say) and will be a game changer. If you are a knowledge worker and design logos, signage, etc... basically anything with text on it you need to incorporate this into your workflow. If you are technical I highly recommend you read the Arxiv paper (https://lnkd.in/gTWuWg24)... love the momentum behind this tech right now! #generativeai , #GenAI, #diffusionmodels , #technology, #innovation, #knowledgework, #AI

🚨Paper Alert 🚨 ➡️Paper Title: Global Structure-from-Motion Revisited 🌟Few pointers from the paper 🎯Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this problem are categorized into incremental and global approaches. 🎯 Until now, the most popular systems follow the incremental paradigm due to its superior accuracy and robustness, while global approaches are drastically more scalable and efficient. 🎯With this work, Authors revisited the problem of global SfM and propose "GLOMAP" as a new general-purpose system that outperforms the state of the art in global SfM. 🎯In terms of accuracy and robustness, they achieved results on-par or superior to COLMAP, the most widely used incremental SfM, while being orders of magnitude faster. 🏢Organization: ETH Zürich , Microsoft 🧙Paper Authors: Linfei Pan, Dániel Baráth, Marc Pollefeys, Johannes Schönberger 1️⃣Read the Full Paper here: https://lnkd.in/g9S-hchi 2️⃣Project Page: https://lnkd.in/gByUSjbX 3️⃣Code: https://lnkd.in/gD3G9Nc9 Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

🚨Paper Alert 🚨 ➡️Paper Title:  MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation 🌟Few pointers from the paper 🎯In this paper authors have introduced MagicVideo-V2 that integrates the text-to-image model, video motion generator, reference image embedding module and frame interpolation module into an end-to-end video generation pipeline. 🎯Benefiting from these architecture designs, MagicVideo-V2 can generate an aesthetically pleasing, high-resolution video with remarkable fidelity and smoothness. 🎯  It demonstrates superior performance over leading Text-to-Video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion model via user evaluation at large scale. 🏢Organization: ByteDance Inc 🔥Paper Authors: Weimin Wang, Jiawei Liu, Zhijie Lin, Jiangqiao Yan, Shuo Chen, Chetwin Low, Tuyen Hoang, Jie Wu, Jun Hao Liew, Hanshu YAN, @Daquan Zhou, Jiashi Feng 1️⃣Read the Full Paper here: https://lnkd.in/g_Q3manq 2️⃣Project Page: https://lnkd.in/gbjnVD3w 🎥 Be sure to watch the attached demo video-Sound on 🔊🔊 Music by Grand_Project from Pixabay Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me, {Naveen Manwani} for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control Another interesting paper on improved camera control. While most users probably expect impressive zero-shot videos from a simple prompt, if you want to create interactive videos you need to give the system more direction * The scene and content to be rendered * The style * The scene lighting * And the camera movement A video of scene is really the combination of subjects and their movement in 3D space, lighting source (and possibly their movement in the same 3D space) and finally the camera movement in 3d space (and possibly it's intrinsics - such as change in focal length or focus plane.) That's a lot and why 3D rendering is so time consuming to create/design. Clearly even when we have solid video generation there will be much more evolution involved to make video generations easier for everyone - not just experts. This is similar to all the add-on like control-net, ip-adapter, etc... for 2D image generation. The paper here does not solve that problem, but it has great references on what is happening in that space that are worth following up on. What is focuses on is being able to generate the same scene from multiple views consistently. As we all know from our movie experience, one very useful technique is switching angles of view during a scene - to focus the view on what is important. This is hard today and what this papers tries to analyze and the neural network architecture changes needed to be able to do so. Quite interesting work IMHO Paper is available here: https://lnkd.in/eRcpz9NV Poster page here with some video examples. Hopefully code will appear soon. https://lnkd.in/esEDh8Bn

This year at #CVPR, Generative Image Dynamics by Zhengqi Li et al. claimed the Best Paper Award, and honestly, it’s no surprise. This work feels like a magic trick for still images, turning them into living, breathing scenes with jaw-dropping realism. A single photo of a tree suddenly swaying gently in the breeze or a candle flickering as if caught in an unseen draft. That’s what their method achieves. By training on real video data, the researchers captured the essence of how natural elements move, modeling it all as "spectral volumes" in the Fourier domain. This allowed them to predict motion in stunning detail, breathing life into static visuals. The applications are as amazing as they sound: turning photos into dynamic videos, crafting perfect looping animations, and even letting users interact with the image, like dragging an object and watching it respond naturally. What blew me away the most is how this paper tackles tricky challenges, like creating smooth, seamless loops without having dedicated training data for it. The results are captivating, and reading about the technology left me in awe. It’s proof that the line between reality and imagination is getting thinner, one still image at a time. Read it here: https://lnkd.in/gHTeBWhg

Ready to explore the cutting edge of text-to-image technology? Discover these powerful diffusion models reshaping the field: FLUX - Master of complex, dynamic scenes. Image Fill - Seamlessly edit and enhance your images. Stable Diffusion XL (SDXL) - Redefining high-resolution imagery. Muse - Google’s model for superior image coherence. eDiff-I - NVIDIA’s go-to for stunning photorealism. DeepFloyd IF - Artistic flair and digital media excellence. GigaGAN - Combining diffusion and GAN for high-impact results. SD3 - The latest in top-tier text-to-image synthesis. Which of these models excites you the most? Let’s discuss how they’re pushing the boundaries of creativity and realism!

What's new in Stable Diffusion 3.5? This update features 3 new models: Large, Large Turbo, and Medium. 📌 Stable Diffusion 3.5 Large is the most advanced model in the series. It excels in producing detailed, high-resolution images (up to 1 megapixel) and adeptly follows intricate text prompts. Features: - Perfect for creating precise, lifelike images or complex art - Designed to work with advanced consumer hardware and professional setups 📌 Stable Diffusion 3.5 Large Turbo is crafted for those who value swiftness while maintaining image quality. Features: - Generates images in just 4 steps - Runs efficiently on standard hardware 📌 Stable Diffusion 3.5 Medium is crafted to work seamlessly on everyday hardware. Features: - Balances quality with customization - Generates images from 0.25 to 2 megapixels in resolution Stay updated with the latest GenAI news with our newsletter: https://lnkd.in/eBZxCMnm

🚀 Transforming Image Synthesis: Insights from Latent Adversarial Diffusion Distillation In the realm of image and video synthesis, the recent breakthrough paper titled "Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation" unveils a groundbreaking approach to overcoming the longstanding challenge of slow inference speeds in diffusion models. As we stand on the brink of a new era in digital imagery, this paper presents not just a leap in technology but a gateway to myriad applications, from enhanced real-time image editing to creating vivid, imaginative realms in seconds. Key Insights: 1. Speed Meets Quality: By introducing Latent Adversarial Diffusion Distillation (LADD), the research provides a novel distillation approach that not only speeds up the image synthesis process but does so without sacrificing the quality of the generated images. This is achieved by cleverly utilizing generative features from pretrained latent diffusion models, significantly simplifying training and enhancing performance for high-resolution, multi-aspect ratio image synthesis. 2. SD3-Turbo: A Game Changer: The application of LADD to Stable Diffusion 3 (SD3) has resulted in the creation of SD3-Turbo. This model stands out by matching the performance of state-of-the-art text-to-image generators with merely four unguided sampling steps, thereby setting a new standard in the field. 3. Versatility and Scalability: The research systematically investigates LADD's scaling behavior and demonstrates its effectiveness across various applications such as image editing and inpainting. This versatility points towards a future where high-quality image synthesis can be seamlessly integrated into a wide range of applications, from digital art to interactive media. Why This Matters: As professionals in the tech and creative industries, staying abreast of such innovations not only inspires us but also opens up new avenues for creativity and efficiency in our work. The ability to generate high-resolution images rapidly and with minimal computational resources could revolutionize content creation, digital marketing, and even AI-driven art. #analyticsvidhya #datascience #machinelearning #researchpaper

During SIGGRAPH 2024 / ACM SIGGRAPH a poster “Animated Ink Bleeding with Computational Fluid Dynamics” by Grzegorz Gruszczyński (IDEAS NCBR), Matt Tokarz (Platige Image), and Przemyslaw Musialski will be presented. 🤝 Science and business can go hand in hand, and we’re proud to present the first result of the scientific cooperation between IDEAS NCBR and Platige Image, which started last year. If you’re interested in this research keep on reading!      🎨 Traditional watercolor art beautifully captures the fluid interplay between ink and water on paper, creating visually complex scenes. Digital artists, inspired by these traditional methods, have attempted to replicate this effect in visual media. However, due to the deficiency of dedicated software tools, these digital versions may be cumbersome. The most challenging is restoring a liquid’s movement given a prescribed pattern. There are currently no tools available on the market that would enable artists to fully control such a process and provide high-quality results simultaneously.      This research is crucial for the animation industry because it eliminates the need for multiple experiments to achieve the desired ink-bleeding effect, which is often used in animations, videos, or games. Digitalization of this effect saves a lot of time – it takes only a few minutes to create a simulation. Additionally, the solution is computationally efficient, making it user-friendly and accessible. #siggraph #ideasncbr #comuptergraphic #ai #research