Innovations Transforming Computer Vision Technology

A new research paper featuring collaborations from NVIDIA, Stanford University, UC San Diego, University of California, Berkeley, and The University of Texas at Austin introduces a breakthrough method that could redefine how we generate long-form videos from textual storyboards. 💡 The Challenge: While modern Transformers have excelled in producing short video clips, generating complex, multi-scene, one-minute videos has remained a hurdle due to the inefficiencies of handling long temporal contexts with traditional self-attention layers. 🔍 The Solution: Introducing Test-Time Training (TTT) layers! This innovative approach integrates neural networks within RNN hidden states, yielding more expressive video generation capabilities. By adding TTT layers to pre-trained Transformers, the team managed to create one-minute videos that maintain coherence across scenes and even complex storylines. 🎬 Proof of Concept: The research team showcased this by utilizing a dataset based on classic Tom and Jerry cartoons. The results highlighted TTT layers outperforming existing approaches like Mamba 2 and Gated DeltaNet, evidenced by a 34 Elo point lead in human evaluations. 🔗 Sample videos, code, and annotations: https://lnkd.in/g3D72gGH #AI #VideoGeneration #MachineLearning #Innovation #Research #TomAndJerry #ArtificialIntelligence #NVIDIA #Stanford #UCBerkeley #UCSD #UTAustin

🎨✨ Niantic, Inc. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 𝗶𝗻 𝟯𝗗 𝗦𝗽𝗹𝗮𝘁 𝘀𝘁𝘆𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝙈𝙤𝙧𝙥𝙝𝙚𝙪𝙨, 𝗮 𝗻𝗲𝘄 𝗺𝗲𝘁𝗵𝗼𝗱 𝗳𝗼𝗿 𝘁𝗲𝘅𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘁𝘆𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝟯𝗗 𝗚𝗮𝘂𝘀𝘀𝗶𝗮𝗻 𝗦𝗽𝗹𝗮𝘁𝘀! Creating immersive, stylized 3D worlds from real-world scenes has always been exciting—but convincingly changing geometry and appearance simultaneously? That's been the tough part. Until now. Morpheus Highlights: ✅ 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗦𝗵𝗮𝗽𝗲 & 𝗖𝗼𝗹𝗼𝗿 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Adjust geometry and appearance separately—unlocking limitless creativity! ✅ 𝗗𝗲𝗽𝘁𝗵-𝗚𝘂𝗶𝗱𝗲𝗱 𝗖𝗿𝗼𝘀𝘀-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 & 𝗪𝗮𝗿𝗽 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗡𝗲𝘁: Ensures your stylizations stay consistent across views. ✅ 𝗔𝘂𝘁𝗼𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗥𝗚𝗕𝗗 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹: Stylizes each frame based on previously edited views for seamless immersion. ✅ 𝗢𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝘀𝘁𝗮𝘁𝗲-𝗼𝗳-𝘁𝗵𝗲-𝗮𝗿𝘁 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 in both aesthetics and prompt adherence, validated by extensive user studies. Imagine turning your neighborhood into a neon cyberpunk cityscape 🌃, a cozy winter lodge ❄️, or even a Minecraft village 🧱—all from just a simple text prompt! This isn't just about stunning visuals—it's about reshaping geometry and appearance independently, opening endless possibilities for immersive experiences. 📝Paper: https://lnkd.in/gGtbWQr3 👉Project: https://lnkd.in/gWWSPNAe 🎥Video: https://lnkd.in/g_KMEMe2 #AI #MachineLearning #ComputerVision #3D #Innovation #Metaverse #GaussianSplats #GenerativeAI

🧠 CAST: Component-Aligned 3D Scene Reconstruction from a Single RGB Image Just explored an impressive research project from ShanghaiTech University and Deemos Technology: CAST, a novel approach to reconstructing high-quality 3D scenes from a single RGB image. 🔍 Key Highlights: Object-Level Segmentation & Depth Estimation: Extracts detailed 2D segmentation and relative depth information. GPT-Based Spatial Analysis: Utilizes a GPT-based model to understand inter-object spatial relationships. Occlusion-Aware 3D Generation: Independently generates each object's full geometry, addressing occlusions and partial data. Physics-Aware Correction: Ensures physical consistency and spatial coherence using a fine-grained relation graph and Signed Distance Fields (SDF). 🎯 Applications: Virtual content creation for games and films. Robotics: Facilitates efficient real-to-simulation workflows. Digital twins and immersive environments. 🔗 Dive deeper into the project here: https://lnkd.in/gHjXBKJE #3DReconstruction #ComputerVision #AI #DigitalTwins #Robotics #CAST #generativeai

𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐢𝐟 𝐲𝐨𝐮𝐫 𝐜𝐚𝐦𝐞𝐫𝐚 𝐜𝐨𝐮𝐥𝐝 𝐭𝐚𝐥𝐤, 𝐭𝐡𝐢𝐧𝐤, 𝐚𝐧𝐝 𝐬𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞.   That’s no longer fiction. It’s Agentic Vision-Language Models (VLMs). AI systems that don’t just see they reason and act. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬: 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: → Detect defects before downtime → Optimize quality inspections → Predict machine failure visually → Reduce downtime 𝐄𝐥𝐝𝐞𝐫𝐥𝐲 𝐂𝐚𝐫𝐞: → Monitor patient movement → Detect fall risks → Alert caregivers instantly → Detect abuse 𝐑𝐞𝐭𝐚𝐢𝐥: → Track shelf gaps → Analyze customer behavior → Automate product placement 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 & 𝐘𝐚𝐫𝐝 𝐅𝐥𝐨𝐰: → Monitor vehicle congestion → Detect safety violations → Optimize yard entry/exit with time stamping → Increase traffic flow operation 𝐀𝐜𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐧: → Identify vision-driven pain points → Pilot VLM Agentic solutions → Upskill teams on Vision-Language AI → Integrate VLM insights into decision workflows → Scale fast with ethical guardrails Seeing is good. Acting intelligently? That’s leadership.    ♻️ Repost to your LinkedIn followers if AI should be more accessible and follow Timothy Goebel for expert insights on AI & innovation. Example will be share tomorrow.   #VisionLanguageModels #AILeadership #OperationalExcellence #FutureOfAI #AgenticAI

A new AI model by UCLA researchers can analyze medical scans 5,000x faster than human doctors with the same accuracy. By using transfer learning from 2D medical data, SLIViT(Slice Integration by Vision Transformer) overcomes the challenge of limited 3D datasets, making it capable of analyzing complex 3D scans with incredible speed and precision. What once took 8 hours now takes just 5.8 seconds. Here’s how it works: 1. Transfer learning SLIViT is pre-trained on extensive 2D medical imaging datasets, enabling it to effectively analyze 3D scans despite the limited availability of 3D datasets. 2. Fast & accurate analysis Using a ConvNeXt backbone for feature extraction and a Vision Transformer (ViT) module for combining these features, SLIViT matches the accuracy of clinical specialists. 3. Flexibility across modalities SLIViT can analyze scans from multiple modalities, including OCT, MRI, ultrasound, and CT, making it adaptable to emerging imaging techniques and diverse clinical datasets. This AI can work with smaller datasets, making it accessible even to hospitals with limited resources. It means: -Rural clinics can offer expert-level diagnostics -Life-threatening conditions are caught earlier -Millions of patients get faster care In healthcare, speed isn’t just about efficiency - it’s about survival. And if SLIViT lives up to its claims in real-world scenarios, it could be a superpower to help save more lives, faster. Could this AI breakthrough reshape the future of medical diagnostics? #ai #innovation #healthtech

His AI turns your iPhone videos into Hollywood-quality 3D scenes in minutes, replacing $100,000 equipment with a smartphone! Meet Taesung Park, who's making professional 3D capture possible with just a smartphone. The journey begins at University of California, Berkeley, where Park pursued his Ph.D. in Computer Science with a focus on computer vision. In 2017, he co-created CycleGAN, a revolutionary AI system that could transform images from one style to another without needing paired training examples. This breakthrough research has been cited over 23,000 times and is now foundational to many AI imaging applications. His work earned recognition at ICCV (International Conference on Computer Vision), marking him as a rising star in computer vision. While working on advanced computer vision problems, Park identified a critical gap: creating high-quality 3D models required expensive equipment and extensive manual work. Traditional methods needed: - $100,000+ in specialized cameras - Weeks of manual processing - Technical expertise - Controlled lighting conditions This led him to found Luma AI in 2020, with a mission to democratize 3D content creation. Luma AI's breakthrough uses Neural Radiance Fields (NeRF) technology to turn regular photos or videos into detailed 3D models by understanding how light interacts with objects from different angles. What sets them apart? - Processes capture 10x faster than competitors - Works with standard smartphone cameras - Requires fewer input images for better results - Uses less computational power - Maintains quality in varying light conditions The technology has found practical applications across industries: - Architects capturing building interiors and exteriors - E-commerce brands creating 3D product displays - Game developers generating environment assets - Real estate agents making virtual property tours - Designers prototyping in 3D Their innovation caught the industry's attention, earning them: - Featured presentation at SIGGRAPH, the world's leading computer graphics conference - Integration partnerships with major 3D platforms - Growing adoption by professional content creators In 2023, Luma AI secured $43 Million in Series A funding led by Andreessen Horowitz, validating its approach to 3D capture technology. This investment is powering their expansion into new applications and platforms. Recent developments include: - Create 3D models straight from your web browser by uploading photos or videos - Enhanced support for the latest iPhone models - Integration with major 3D content platforms like Unity, Unreal Engine, Sketchfab, and Blender Park's vision goes beyond just technology - he's making professional-quality 3D capture accessible to everyone with a smartphone, transforming how we create and interact with digital content. Was this inspiring? 🔁 Repost if it was. 💻 Follow #AIwithAnurupa to stay updated with everything AI. #AI #founders #startup #technology

Check out this Stereo4D paper from DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for VR headsets. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. The cool part is it tracks how objects move between frames while also reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways.

I am thrilled to announce my latest paper on "Generative AI for Vision: A Comprehensive Study of Frameworks and Applications." This paper explores real-world applications of Generative AI in computer vision and introduces a new categorization of image generation techniques based on input types—designed to facilitate AI-driven application development. Key takeaways: -> A structured classification of image generation methods for practical use. -> Breakdown of Generative Adversarial Networks and their types (pix2pix, CycleGAN, etc.), diffusion models, VAEs, and conditional frameworks. -> How text-to-image, image translation & multimodal AI are transforming industries. ->Challenges like computational costs, bias, and ensuring AI aligns with user intent. Full article in the comments! Feel free to share it with your network. Let’s drive AI innovation forward! #GenerativeAI #ComputerVision #AIResearch #DiffusionModels #GANs #MultimodalAI #ArtificialIntelligence #AI #AIAgents #LLMAgents #Innovation #MachineLearning #ResponsibleAI #LLM #AIApplications #MachineLearning

Innovative Camera Inspired by the Human Eye Revolutionizes Robotic Vision! A groundbreaking camera mechanism takes inspiration from the human eye to enhance how robots perceive and interact with their environment. The Artificial Microsaccade-Enhanced Event Camera (AMI-EV) mimics the eye’s tiny involuntary movements, known as microsaccades, to maintain clear and stable vision even with rapid motion. 🔍 Key Features of AMI-EV: Microsaccades Simulation: A rotating prism inside the camera redirects light beams, stabilizing the image similar to the human eye. High Frame Rate: Captures motion in tens of thousands of frames per second, surpassing typical commercial cameras. Versatile Applications: From self-driving cars and robotic vision to augmented reality and space astronomy, this innovation opens new possibilities. 🧠 "Our eyes take pictures of the world around us and are sent to our brain, where the images are analyzed. When working with robots, replace the eyes with a camera and the brain with a computer. Better cameras mean better perception and reactions for robots," explained Yiannis Aloimonos, UMD professor and co-author of the study. 🌐 Broader Impact: Smart Wearables: Ideal for virtual reality applications due to superior performance in extreme lighting conditions, low latency, and low power consumption. Human Pulse Detection & Rapid Movement Identification: Early testing shows the camera’s capability in various contexts. This innovative camera system is paving the way for more advanced and capable systems, enhancing everything from autonomous driving to smartphone cameras. 🔗 Read more about this revolutionary technology in Science Robotics Sources: University of Maryland. "Computer scientists develop new and improved camera inspired by the human eye." BOTAO HE, Yiannis Aloimonos, Cornelia Fermuller, Jingxi Chen, Chahat Deep Singh. (Credit for images and research) #Robotics #ComputerVision #AI #Engineering #Automation