🤖 Can Codex Build a Human Pose Estimation App on reComputer R? From Natural Language Prompts to Edge AI in Action! Can Codex build and deploy a Human Pose Estimation app on the reComputer R? We put it to the test. With just a few prompts, Codex connected via SSH, built a YOLOv8 Pose Estimation WebUI, and deployed it directly to the device, powered by Hailo-8 acceleration. 🎯 Upload a video. Detect human poses. Visualize body keypoints and skeletons right in your browser. Less manual coding, faster prototyping, and Edge AI running where it matters. 🛠️ Check this project on reComputer AI Lab: #SeeedStudio# #reComputerR# #RaspberryPi# #reComputer# #VisionAI# #EdgeAI# #AIoT# #ComputerVision# #reComputerAILab# #AIDeployment# #PowerfulRaspberryPiAIBox# #TheAIHardwarePartner#

🚀 Deploy YOLO11 on RK3576 Module + Raspberry Pi CM4 IO Board!! We tested the #reComputer# #Rockchip# #RK3576# Module on a #RaspberryPi# #CM4# IO Board and successfully deployed #YOLO11# from #reComputerAILab# for real-time object detection! ✅ RK3576 NPU acceleration ✅ YOLO11 running locally on the edge ✅ USB camera support via CM4 IO Board ✅ Armbian + Docker deployment 🧩 Check reComputer RK3576 Module: 🛠️ Try reComputer AI Lab: 🛒 reComputer RK3576/RK3588 is in STOCK: #SeeedStudio# #reComputerRK# #Rockchip# #reComputer# #RK3576# #VisionAI# #EdgeAI# #AIoT# #ComputerVision# #reComputerAILab# #AIDeployment# #TheEasierPowerfulAIComputer# #TheAIHardwarePartner#

👁️ What if every camera could become an intelligent eye? That is the promise of Computer Vision + AI. With an AI Box, intelligence can move closer to the camera — enabling video streams to be analysed locally for events, patterns and actionable insights. From security & surveillance to retail, transportation, infrastructure and smart cities, the evolution is happening: Camera → AI Box → Computer Vision → Insight → Action At Transline, we are bringing together CCTV, Biometrics, AI, Computer Vision and Edge AI through our technology ecosystem and NETRA stack. The future isn't just about capturing more video. It's about understanding what the video means. See. Understand. Respond. The camera is becoming a sensor. The AI Box is becoming the brain. And Computer Vision is becoming the intelligence layer. 👁️🧠 #ComputerVision# #AI# #EdgeAI# #AIBox# #VideoAnalytics# #CCTV# #Biometrics# #NETRA# #ArtificialIntelligence# #DigitalIndia#

🧵 New repo: Awesome Egocentric Dataset — a curated list of first-person vision datasets, refreshed beyond the classic Ego4D-era list. ✅ Recent 2023–2026 releases (Ego-Exo4D, EgoSchema, AEA, EgoExoLearn, EgoLife…) ✅ Dead/broken links removed, migrated ones fixed ✅ A section on egocentric-driven VLA papers (training source + data-processing), with first-principles + fishbone analysis 🚀 Open-source, contributions welcome. 🔗 #EgocentricVision# #FirstPersonVision# #VLA# #ComputerVision# #Datasets#

TL;DR FoundationStereo, the high-accuracy zero-shot stereo model, sped up by over 10x with almost no accuracy loss. It finally runs in real time. Title: Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching URL: Points 🎯 Problem: stereo had split into "accurate but heavy foundation models" vs "fast methods that sacrifice accuracy and generalization" 🧩 Divide-and-conquer: each of three bottlenecks accelerated separately (feature extraction, cost filtering, refinement) 📚 Feature extraction: distill hybrid monocular + stereo priors into a single efficient student 🔍 Cost filtering: blockwise NAS over 8 blocks cuts search from O(n^N) to O(n), with ILP minimizing error under a latency budget ✂️ Refinement: structured pruning via ConvGRU recurrent dependency graphs and Taylor-expansion importance 🗂 1.4M in-the-wild stereo pairs auto pseudo-labeled with a normal-consistency check ⚡ Middlebury-Q: 4.51% BP-2 at 49ms (vs 2.64% at 496ms, 10.1x slower), down to 21ms with TensorRT A new SOTA among real-time stereo methods, well suited for robotics and AR deployment. #StereoMatching# #ComputerVision#

We were honored to host Dr. Nadia Brancati(NRCI), for a guest seminar on May 28th! 🇮🇹 She shared incredible insights from her cutting-edge work in Computer Vision, Machine Learning, and Medical Imaging. Thank you for an inspiring talk! #ComputerVision# #MedicalAI# #MachineLearning#

Seeing InternVideo-Next explicitly characterize its architecture as "Encoder–Predictor–Decoder (EPD)" and defining the Predictor as a "latent world model" confirms a clear convergence in the field. 📉 From our early work on Context AutoEncoder (CAE, — where we decoupled the Encoder, Predictor ("Regressor" in the paper), and Decoder — to Meta's I-JEPA/V-JEPA shifting entirely to latent prediction, and now this. It seems we are all validating the same core intuition: Understanding ≠ Pixel Reconstruction. 💡 Decoupling representation learning from high-frequency detail generation to build World Models in latent space is evidently the path forward. Glad to see our early intuition resonating with the latest SOTA. 🫡 #AI# #ComputerVision# #WorldModel# #JEPA# #CAE#

The Vesuvius Challenge is still a CHALLENGE! 🧠📜 We’re teaming up with @kaggle to launch a $100K Surface Detection competition on 3D CT scans of the Herculaneum scrolls: . Train topology-aware models, climb the leaderboard, and help us read an ancient library! #VesuviusChallenge# #kaggle# #computervision# #machinelearning#

Can you generate a video that spins a product or person a full 360° and still keeps it "the same thing"? 🔄 OmniShowcase: Omnidirectional Subject-Consistent Video Generation from Multi-View Images It generates subject-consistent orbit videos from four multi-view reference images. 🧱 Highlight 1: auto-synthesized 3D-consistent data From one 3D mesh, it renders the target orbit and four reference views at 90° intervals. Backgrounds come from video inpainting, with a VLM gating every stage. Curation costs about $120 in total, and the roughly 4,000-video Showcase-4K is planned for release. 📐 Highlight 2: normal maps as in-context input It builds 3D Gaussians from the four references and feeds a normal-map turntable to Wan2.1 via LoRA. No frame-level alignment with the target is needed, and going from one to four references lifts DINO from 0.702 to 0.799. 📊 Highlight 3: a wide margin on 3D metrics On ShowcaseBench versus 3DreamBooth, GT-CD improves from 0.1717 to 0.0824 and MEt3R from 0.325 to 0.190. In the user study it won 70.9–79.4% of votes on each criterion. The takeaway: explicit 3D geometry beats adding more 2D references. Non-rigid subjects remain a weakness. #VideoGeneration# #ComputerVision#