【4K60FPS opticalflow】 特殊相対性理論によると、水着ナガを光速の99.5％まで加速させれば時間の進み方は10分の1になる。つまり合法的に水着ナガの供給精度を10倍にできるという事。

Researchers monitor these terms to track updates in spatial inpainting consistency and real-time texture patching software. * #VideoForensics / #ImageForensics: The core domain tags used by forensic specialists to share methodologies for identifying pixel anomalies, compression discrepancies, and metadata inconsistencies within disputed video files. * #OSINT (Open Source Intelligence): The operational framework under which global analysts collaborate to verify the geographical and temporal authenticity of broadcast media, often cross-referencing live video details with physical maps, satellite data, and weather patterns. * #StreamEdit / #RealTimeAI: Indicators tracking the technical implementation of low-latency, frame-by-frame generative pipelines, focusing specifically on hardware optimization and transformer-based model updates. * #TemporalConsistency / #OpticalFlow: Analytical markers used within the computer vision community to discuss the elimination of flickering artifacts and the stabilizing of synthetic overlays in dynamic environments. #Forensic Countermeasures and Detection Methodologies Exposing real-time video manipulation requires looking past the surface appearance of the footage and analyzing its underlying mathematical and structural properties. Digital forensic investigators use several specialized techniques to identify subtle anomalies left behind by generative inference layers. ┌──> Photo-Response Non-Uniformity (PRNU) Sensor Noise Analysis [Suspicious Video Feed] ┼──> Spatial Inconsistency & Pixel Artifact Invalidation └──> Temporal/Chrominance Frequency Discontinuity Analysis 1. Sensor Noise Fingerprinting (PRNU Analysis) Every physical camera sensor possesses microscopic variations introduced during manufacturing. These variations create a unique noise pattern known as Photo-Response Non-Uniformity (PRNU), which acts as a digital watermark embedded across every frame the camera captures. [Raw Frame] ──> [PRNU Extraction Filter] ──> [Uniform Noise Field] (Authentic) [Edited Frame] ──> [PRNU Extraction Filter] ──> [Discontinuous / Erased Noise Field] (Tampered) When a generative AI model inpaints a region of a frame or replaces an object, it synthesizes new pixels mathematically. These synthetic pixels lack the camera's original PRNU hardware signature. By passing video frames through specialized high-pass noise extraction filters, forensic investigators can map the PRNU distribution. If a specific region of the screen—such as a background wall or a item on a table—displays a sudden absence of sensor noise or shows a distinct, uniform noise pattern, it indicates that the area has been digitally reconstructed. 2. Spatial Artifact Detection and Pixel Discontinuity Even with advanced photometric alignment, generative models frequently introduce minute spatial errors along the boundaries where authentic imagery meets synthetic imagery: * Edge Blending Anomaly Analysis: Algorithms analyze the spatial frequency of object edges. Real objects display a natural, consistent gradient transition between their boundaries and the background, determined by the camera lens's modulation transfer function. AI-inserted or removed objects often exhibit microscopic blur zones or sharp pixel-step discontinuities where the generative mask was applied. * Compression Signature Invalidation: Video compression codecs split frames into small macroblocks (typically 8×8 or 16×16 pixel grids) to execute discrete cosine transforms (DCT). When an intercept model modifies a frame before final encoding, it disrupts the natural macroblock boundary alignment. Forensic software can visualize the Error Level Analysis (ELA) of the video, highlighting regions where the compression ratios diverge significantly from the baseline frame metrics.

センサーのメーカのArduCopterでのOpticalFLOW実践動画を見るに、 手で突くとそのままのように見えるけど、 ゆっくりと復帰しているようにも見える。 しかし、モニターでの数値的根拠が見えてないので謎、、 youtube.com/watch?v=D-ooFHEt…

IYPpresents 『Get back to basics Vol.26 』 2026/5/30(土)@bessie_sapporo start 17:30 adv:¥2,000 1D¥500 Act :   ◆HFサクセション(RCサクセション) ◆SiN(SiM)  ◆G6(GLAY) ◆OPTICALFLOW(IRON MAIDEN) ◆THE SOMERS(布袋寅泰)  ◆Moth(ZIGGY)

🆕 Patent application #US20260024217A1 by #NVIDIA reveals #OpticalFlow-based frame interpolation for sensor synchronization. 📷 Systems detect motion across image frames to align asynchronous frames with a target time, using an optical flow accelerator. This tech enhances data collection from sensors like cameras, LIDAR, & RADAR for operations like stitching or reconstruction. #AI #ImagingTech $NVDA #automotive

repost @johnbcarpenter on IG This is about a minute of our #tuxedo #cat helping me test out a #p5js sketch for #USC #IML404 // this is a #computervision #algorithm called #opticalflow // will post a link to the code when it’s cleaned up // first class is this week

OpticalFlow⚡️

Thank you. VIO is running at 10-15hz video 200hz imu on a single-core rockchip arm7. It’s a mix of opticalflow and orb descriptors. biggest bottleneck is backend running purely on this not-so-fast cpu. I wish there was an arm8 rockchip in same price-point.

#summerexperiments using optical flow to detect the movement of the human and translates it into generative AI imagery 🤖 From movement to poetry, from displacement to bloom 🌿🍀 #StableDiffusion #opticalflow #AIArt #GenerativeArt #python #pythonprogramming

OpticalFlow⚡️

先ほど、無事承認されてた！ 基本的に使い方はGeoTrackerと同じだけどOpticalFlowのキャッシュだけチュートリアルのように相対パスか任意のパスと名前で.dbって名前で保存設定が必要なのだけ初回分かりづらい。なお、GeoTrackerは/tmpに自動パス通るかキャッシュ取らなくても一応動作する点が違う。

14日旭川の#MOSQUITOで楽しいLIVEでした。 主催様、出演者様、スタッフ様、足を運んで下さいました皆様ありがとうございました🥰 #OPTICALFLOW

Excited to share our #CVPR2025 paper, "GG-SSMs: Graph-Generating State Space Models", to be presented as a Highlight Paper in Nashville this week! PDF: arxiv.org/abs/2412.12423 Code: github.com/uzh-rpg/gg_ssms While #StateSpaceModels (#SSMs) are extremely powerful for sequential data, the one-dimensional processing paradigm severely limits their ability to model non-local interactions in high-dimensional data. Even advanced models like #Mamba, #Vim, and #VMamba, though offering selective scanning, remain constrained by predetermined paths, often failing to efficiently capture the high-dimensional interactions of data dependencies. Our new Graph-Generating State Space Models (GG-SSMs) directly address this limitation. We introduce a novel framework that dynamically constructs graphs based on inherent feature relationships, adapting to the unique structure of the data itself! By leveraging Chazelle's Minimum Spanning Tree (MST) algorithm, known for its near-linear time complexity, GG-SSMs enable robust feature propagation and efficiently model complex, long-range dependencies without prohibitive computational costs. Our contributions are as follows: * Integration of dynamic graph structures directly into the SSM framework, capturing complex spatial and temporal relationships. * SOTA results across 11 diverse datasets, including ImageNet, optical flow, event-based eye tracking, and time series! - ImageNet: Sets a new benchmark with 84.9% top-1 accuracy, outperforming prior SSMs by 1%. - KITTI-15 Optical Flow: Achieves the lowest error rate ever reported at 2.77%. - Eye Tracking: Improves detection rates by up to 0.33% with fewer parameters on event-based datasets. - Time Series: Demonstrates superior forecasting accuracy across six real-world datasets. * MST-based construction ensures linear computational complexity, making GG-SSMs highly scalable and efficient for large-scale applications. Kudos to @NikolaZubic5 ! Reference: Nikola Zubić and Davide Scaramuzza, GG-SSMs: Graph-Generating State Space Models, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 2025., PDF: arxiv.org/abs/2412.12423 Code: github.com/uzh-rpg/gg_ssms Highlight Presentation. @ERC_Research @uzh_ifi @UZH_en @UZH_Science #GraphNeuralNetworks #ComputerVision #MachineLearning #AIResearch #DeepLearning #OpticalFlow #TimeSeries

🔧Premiere Pro 時間補間法活用術🔧 ✅ フレーム補間でスムーズなスローモーション ✅ 速度リマッピングでシーンに動的な変化 【基本フロー】 ・クリップ選択 ・速度設定 ・Optical Flowで微調整 これで編集効率＆クオリティが激変！💥 #PremierePro #動画編集 #OpticalFlow

Zibert disse em entrevista pro adrenaline que possivelmente o multi FG pode chegar nas RTX40 e o cabeludo da nvidia fala praticamente a mesma coisa sobre o frame generation atual... Ou seja, não precisa mais de opticalflow pra fazer o FG, logo series 20 e 30 podem suportar, será?