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Motion capture is about to get easy. This is captured with a single iPhone using Move One, our new mobile app that uses AI to extract motion data from video. No suits or markers required. We are in invite only beta right now, waitlist link in the comments📱

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Move AI

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200,998 просмотров • 2 лет назад •via X (Twitter)

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Its time for motion capture to be accessible to everyone. Reply with how you will use Move One and we’ll give you early access 🙌 Single camera motion capture with your iPhone. Shoot anywhere you want. No suits or markers required.
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Its time for motion capture to be accessible to everyone. Reply with how you will use Move One and we’ll give you early access 🙌 Single camera motion capture with your iPhone. Shoot anywhere you want. No suits or markers required.

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Reply to this tweet with how you will use Move One and we’ll give you early access 🙌 Single camera motion capture with your iPhone. Shoot anywhere you want. No suits or markers required.
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Reply to this tweet with how you will use Move One and we’ll give you early access 🙌 Single camera motion capture with your iPhone. Shoot anywhere you want. No suits or markers required.

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Bringing performances to the digital world is now easier than ever. This is a behind the scenes look at 3D animator Keymyon Kim using Move Multi-Cam to capture and create the #BaddieChallenge. Motion capture powered by AI. No suits or markers required.
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Bringing performances to the digital world is now easier than ever. This is a behind the scenes look at 3D animator Keymyon Kim using Move Multi-Cam to capture and create the #BaddieChallenge. Motion capture powered by AI. No suits or markers required.

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This is a new era for 3D animation. Move One for Teams enables creatives to capture and create 3D animations with just their phone. Motion capture anywhere, powered by AI. No suits or expensive hardware required.
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This is a new era for 3D animation. Move One for Teams enables creatives to capture and create 3D animations with just their phone. Motion capture anywhere, powered by AI. No suits or expensive hardware required.

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This is a virtual catwalk powered by AI. The London College of Fashion used Move AI to generate 3D animation from 2D video. 5 people captured simultaneously. No suits or markers required.
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This is a virtual catwalk powered by AI. The London College of Fashion used Move AI to generate 3D animation from 2D video. 5 people captured simultaneously. No suits or markers required.

Move AI

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animators are not needed anymore this 3D AI motion capture plugin can convert character movement from real video to 3D data and.. you can apply the motion to any 3D character.. link in comments
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animators are not needed anymore this 3D AI motion capture plugin can convert character movement from real video to 3D data and.. you can apply the motion to any 3D character.. link in comments

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64,585 просмотров • 8 месяцев назад

Pure Precision. Move AI is pushing 3D character animation and motion capture forward with these powerful releases: – Dex by Move AI: significantly improved hand & finger tracking – New desktop apps: Move Pro now available on Windows, macOS, and Linux – Editor (Beta): fine-tune motion input data with precision Built for animators, developers, and researchers who demand lifelike motion — with total control, without suits. Watch the full video here →
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Pure Precision. Move AI is pushing 3D character animation and motion capture forward with these powerful releases: – Dex by Move AI: significantly improved hand & finger tracking – New desktop apps: Move Pro now available on Windows, macOS, and Linux – Editor (Beta): fine-tune motion input data with precision Built for animators, developers, and researchers who demand lifelike motion — with total control, without suits. Watch the full video here →

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Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required. Learn more about Act-One below. (1/7)
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Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required. Learn more about Act-One below. (1/7)

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Introducing Move One, an easy way to capture and create 3D animations with just a phone #MadeWithMove 🎥 Capture motion anywhere with a single camera ⚡️ Get your data back in minutes 🤖 Animate 3D characters Try for free👇
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Introducing Move One, an easy way to capture and create 3D animations with just a phone #MadeWithMove 🎥 Capture motion anywhere with a single camera ⚡️ Get your data back in minutes 🤖 Animate 3D characters Try for free👇

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Video Generation is LIVE on imgnAI Discord and Web in Beta! You can now bring your AI-generated images to life with short videos - right inside our Discord bot and web app. This is just the beginning of a new era in creative motion. 👇 🧵
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Video Generation is LIVE on imgnAI Discord and Web in Beta! You can now bring your AI-generated images to life with short videos - right inside our Discord bot and web app. This is just the beginning of a new era in creative motion. 👇 🧵

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UE5.6 real-time motion capture plugin with native MetaHuman support. It requires only a webcam or uploaded video, and uses a 1-billion-parameter model to convert video into 3D motion data in real time, enabling automatic retargeting.
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Another great music video by Kirill Nong using Move AI Multi-Cam. 🔥 Our motion capture tech uses AI to make the creation of lifelike 3D animations super easy with absolutely amazing results. 🚀 Experience the magic of effortless animation today! ⚡️
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Another great music video by Kirill Nong using Move AI Multi-Cam. 🔥 Our motion capture tech uses AI to make the creation of lifelike 3D animations super easy with absolutely amazing results. 🚀 Experience the magic of effortless animation today! ⚡️

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Now that the Gemini app is on iPhone, here are some of our favorite mobile features to get you started. Try the new Gemini app on your iPhone →
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VideoJAM is our new framework for improved motion generation from AI at Meta We show that video generators struggle with motion because the training objective favors appearance over dynamics. VideoJAM directly adresses this **without any extra data or scaling** 👇🧵
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Security Camera App: DVR Recorded Video Playback Watch this video to see how easy it is to remotely playback recorded video surveillance footage on a Viewtron DVR with the mobile app for iPhone and Android. You can do this at the location of your security camera system using a WIFI connection or remotely over the Internet from anywhere in the world. It's easy to check in on your property using a Viewtron camera system when you are traveling. You can learn more about the Viewtron mobile app here.

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A video is compressed 4D data. 🌍 Today, we’re sharing a preview of our latest research: 4D reconstruction from a single monocular video. 🎬 From one casually captured video, our system reconstructs a dynamic, freely viewable 4D Gaussian scene—recovering geometry, appearance, camera motion, and object motion across space and time. No multi-camera rig. Just video → 4DGS. The world has already been captured in video. The next step is to unlock the 4D structure inside it. Watch the result below. 👇 #4DGS #GaussianSplatting #CVPR #CVPR2026 #AIGC #AI #3DGS

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Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.
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