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Introducing Gen 2 spatial motion: 3D motion capture, full-body dynamics, joint torques, ground reaction forces, advanced motion retargeting, motion prediction. Works on AI video, phones, cinema cams, stadia. Launched at Movement Day, BAFTA London.

Move AI

22,394 subscribers

68,681 görüntüleme • 1 yıl önce •via X (Twitter)

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𝐙𝐞𝐧𝐠 💜 profil fotoğrafı

𝐙𝐞𝐧𝐠 💜1 yıl önce

Wow. This is next level!

OPEN profil fotoğrafı

OPEN1 yıl önce

Cinematic pedigree of the highest order meets innovative AAA gameplay in OP3N. Dive into the action by wishlisting on Epic Games TODAY!

Brett Stuart profil fotoğrafı

Brett Stuart1 yıl önce

Wonder Dynamics gunna get run for their money. Looking great!

K Slash profil fotoğrafı

K Slash1 yıl önce

Can we use this today?

HaloVFX profil fotoğrafı

HaloVFX1 yıl önce

Lets go!! Huge update

Madness Apex profil fotoğrafı

Madness Apex1 yıl önce

🤯

Kabooki AI profil fotoğrafı

Kabooki AI1 yıl önce

Looks great!

. profil fotoğrafı

.1 yıl önce

How can I use it?

Pupologic profil fotoğrafı

Pupologic1 yıl önce

Where and how can we use the Gen2?

Cr4zy3y3z profil fotoğrafı

Cr4zy3y3z1 yıl önce

Blender integration 😍

neoCortez profil fotoğrafı

neoCortez1 yıl önce

This is awesome. Can’t wait to test it out

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PICO Motion Tracker and VRChat have joined forces! On the launch day of PICO Motion Tracker, get ready to immerse yourself in the VRChat universe with full-body motion capture. Unleash your creativity, express yourself like never before. #PICOMotionTracker #VRChat #VRRevolution

PICO Motion Tracker and VRChat have joined forces! On the launch day of PICO Motion Tracker, get ready to immerse yourself in the VRChat universe with full-body motion capture. Unleash your creativity, express yourself like never before. #PICOMotionTracker #VRChat #VRRevolution

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Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

Full-body joint tracking + smart gloves capture every nuance of human motion, which is retargeted in real-time to a humanoid robot (here, Tesla Optimus) for smooth teleoperation. This is the power of inertial motion capture. More than control, mocap is the gold standard for collecting high-fidelity humanoid training data. Advanced AI algorithms transform raw accelerometer streams into precise 3D joint poses, turning human movement into lifelike robot action.

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Unitree Humanoid Robots Updated With Open-Source Full-Body Motion Dataset 🤗 We’ve adapted motion capture data for Unitree H1, H1-2, and G1 humanoid robots, using numerical optimization methods to make their movement trajectories more natural. Open-Source： #AI #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI #Dance

Unitree Humanoid Robots Updated With Open-Source Full-Body Motion Dataset 🤗 We’ve adapted motion capture data for Unitree H1, H1-2, and G1 humanoid robots, using numerical optimization methods to make their movement trajectories more natural. Open-Source： #AI #OpenSourceDataset #Unitree #EmbodiedAI #Humanoid #DataCollection #AGI #Dance

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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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Move AI

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We present EgoReAct: Real-time 3D human reaction generation from streaming egocentric video. 🌟Reacting to streaming egocentric video is something humans do every day. We hope EgoReAct makes human motion more human-like. 🔎 What we found: existing ego-reaction data can be spatially inconsistent (e.g., moving reactions paired with fixed-camera videos), which breaks 3D grounding. 📷 What we built: HRD, a spatially aligned egocentric video–reaction dataset (3,500 pairs, 32 categories), plus a spatially aligned ViMo fix for fair evaluation. (Instead of collecting expensive ground-truth motion, we employ VDM to generate the egocentric videos.) 👁️⚡🏃 Our simple yet effective pipeline: motion tokenization for compact discrete codes + an autoregressive Transformer for online, strictly-causal generation. Metric depth and head dynamics further improve 3D spatial consistency. Project Page: ArXiv: #HumanMotion #EgocentricVision #3D #ARVR #Animation #AIGC #DeepLearning #GenerativeAI #Graphics #ComputerVision #Motion

We present EgoReAct: Real-time 3D human reaction generation from streaming egocentric video. 🌟Reacting to streaming egocentric video is something humans do every day. We hope EgoReAct makes human motion more human-like. 🔎 What we found: existing ego-reaction data can be spatially inconsistent (e.g., moving reactions paired with fixed-camera videos), which breaks 3D grounding. 📷 What we built: HRD, a spatially aligned egocentric video–reaction dataset (3,500 pairs, 32 categories), plus a spatially aligned ViMo fix for fair evaluation. (Instead of collecting expensive ground-truth motion, we employ VDM to generate the egocentric videos.) 👁️⚡🏃 Our simple yet effective pipeline: motion tokenization for compact discrete codes + an autoregressive Transformer for online, strictly-causal generation. Metric depth and head dynamics further improve 3D spatial consistency. Project Page: ArXiv: #HumanMotion #EgocentricVision #3D #ARVR #Animation #AIGC #DeepLearning #GenerativeAI #Graphics #ComputerVision #Motion

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Free Blender AI motion capture plugin — also compatible with UE and Unity. Connects to a 1080P webcam and runs locally with a 1-billion-parameter model, requiring 8GB of GPU memory for real-time processing. Supports single-camera real-time capture and video uploads with no limit on the number of uploads. Includes full-body, facial, and finger motion capture. Completely free to use.

Free Blender AI motion capture plugin — also compatible with UE and Unity. Connects to a 1080P webcam and runs locally with a 1-billion-parameter model, requiring 8GB of GPU memory for real-time processing. Supports single-camera real-time capture and video uploads with no limit on the number of uploads. Includes full-body, facial, and finger motion capture. Completely free to use.

CYANPUPPETS

57,643 görüntüleme • 1 yıl önce

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: 🌐Webpage: 💻 Code: 🕸️Model: 📊 Dataset: 👉 Interactive Demo: Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series:

Boyi Li

66,999 görüntüleme • 7 ay önce

Most AI video work is just "generate and hope." This is different. Creator seungho__yeo ( IG ) rebuilt a live drum performance from the ground up. Using the drummer's actual motion data to redesign background mood, lighting, and spatial depth frame by frame. The pipeline: → Motion Tracking → Depth Mapping → AI Edit ( Seedance 2) → AI Relighting → Background Reconstruction → Cinematic Color Grading All built inside ComfyUI. This is what intentional AI production looks like. Not more generations, better direction. This is the level ComfyUI creators are working at.

Most AI video work is just "generate and hope." This is different. Creator seungho__yeo ( IG ) rebuilt a live drum performance from the ground up. Using the drummer's actual motion data to redesign background mood, lighting, and spatial depth frame by frame. The pipeline: → Motion Tracking → Depth Mapping → AI Edit ( Seedance 2) → AI Relighting → Background Reconstruction → Cinematic Color Grading All built inside ComfyUI. This is what intentional AI production looks like. Not more generations, better direction. This is the level ComfyUI creators are working at.

ComfyUI

28,385 görüntüleme • 1 ay önce

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,708 görüntüleme • 7 ay önce

🚨 Biggest Drop Alert! 🚨 Motion Transfer, powered by Runway Act 2, is officially live in ImagineArt 📷 ✨ Here’s how it works: 1️⃣ Add a base video to define the movement. 2️⃣ Choose your character. 3️⃣ Watch your character instantly takes on the motion from the video inside ImagineArt From dance moves to cinematic shots, the possibilities are endless. This is where creativity meets next-level AI. Try it out now in the New ImagineArt and bring your imagination to life!

🚨 Biggest Drop Alert! 🚨 Motion Transfer, powered by Runway Act 2, is officially live in ImagineArt 📷 ✨ Here’s how it works: 1️⃣ Add a base video to define the movement. 2️⃣ Choose your character. 3️⃣ Watch your character instantly takes on the motion from the video inside ImagineArt From dance moves to cinematic shots, the possibilities are endless. This is where creativity meets next-level AI. Try it out now in the New ImagineArt and bring your imagination to life!

ImagineArt

437,717 görüntüleme • 10 ay önce

KLING 2.6 MOTION CONTROL is LIVE on Higgsfield. TRY FREE. DAY-0 access. As always. Fast & complex movements finally work in AI video. Full-body sync, expression mapping, 30-second generations. Everyone else is behind. For 9 hours: retweet & reply & like & follow for 249 credits

KLING 2.6 MOTION CONTROL is LIVE on Higgsfield. TRY FREE. DAY-0 access. As always. Fast & complex movements finally work in AI video. Full-body sync, expression mapping, 30-second generations. Everyone else is behind. For 9 hours: retweet & reply & like & follow for 249 credits

Higgsfield AI 🧩

1,581,028 görüntüleme • 7 ay önce

Check out this drone: Joshua Bird built this drone ($20) in his dorm! [open-source motion capture system⬇️] Built at low cost, a motion capture system for tracking & and flying drones autonomously, with millimeter-level precision at room-scale. The student used $1 PS3 Eye cameras with 150fps capability. The challenge? PID tuning! It took him 4 days of crashes to get the drone to hover, but it's still wobbly. Used a 3x nested PID loop for precise control. This project led to his dissertation on visual SLAM! Full details - Algorithms for camera positioning & obstacle triangulation, in his YouTube video: Code & 3D files on GitHub: More projects at

Check out this drone: Joshua Bird built this drone ($20) in his dorm! [open-source motion capture system⬇️] Built at low cost, a motion capture system for tracking & and flying drones autonomously, with millimeter-level precision at room-scale. The student used $1 PS3 Eye cameras with 150fps capability. The challenge? PID tuning! It took him 4 days of crashes to get the drone to hover, but it's still wobbly. Used a 3x nested PID loop for precise control. This project led to his dissertation on visual SLAM! Full details - Algorithms for camera positioning & obstacle triangulation, in his YouTube video: Code & 3D files on GitHub: More projects at

Ilir Aliu

101,689 görüntüleme • 1 yıl önce

Many amateurs wait until the top of the backswing to initiate the downswing. Elite players do not. When we look at force plate data from the best ball strikers in the world, the majority begin pushing against the ground well before the backswing is complete. These ground reaction forces help decelerate the pelvis so the player can lead with the lower body on the downswing. Force precedes motion. New video on our YouTube channel. Link below

Many amateurs wait until the top of the backswing to initiate the downswing. Elite players do not. When we look at force plate data from the best ball strikers in the world, the majority begin pushing against the ground well before the backswing is complete. These ground reaction forces help decelerate the pelvis so the player can lead with the lower body on the downswing. Force precedes motion. New video on our YouTube channel. Link below

TPI

23,534 görüntüleme • 16 gün önce

$P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐$

P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: We’re launching with 70% discounted usage for the coming two days with our launch partners. each::labs, Replicate, Runware, Scenario, WaveSpeedAI, Wiro, Tellers, Soon also on Together AI We’d love your feedback as you try it. 📚 Model docs & API examples: 📚 API quickstart: ⭐️ OSS: 🌐

Pruna AI

22,727 görüntüleme • 1 ay önce

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

Alibaba presents MIMO Controllable Character Video Synthesis with Spatial Decomposed Modeling Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip to three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results demonstrate effectiveness and robustness of the proposed method.

AK

148,998 görüntüleme • 1 yıl önce

On Figma Motion, my demo & review. This is one of those updates that clicked swiftly into my workflow. Like in my 3D pipeline, using this stick shift animation, I emphasize hardware feedback with motion from Figma Motion. Assets are animated in Fig Motion and loaded into Blender as video textures for mesh and material control. It's advantageous since it is built into an ubiquitous ecosystem. A lot of work these days start in Figma even for someone like me that works outside the tool frequently. Can't over-emphasize my appreciation for the schema used to introduce motion to nearly all properties. Reminds me of how motion works in Blender - with better complexity management. Better than introducing more menus for those. Ease curves iD between keyframes is also an improvement over existing systems. Having spent some time on the tool, here are updates I'll like to see to Figma Motion. These are based on my immediate needs as at the time of working on this demo: • Using modifier keys (CMD, Shift, Ctrl) to jump / nudge across the timeline. E.g: holding down Shift key to make the playhead jump 100ms etc. • Jump to previous or next keyframe. • Better anchor point trails • Renaming layers from the timeline as well. • Editable custom bezier styles: after saving a style, I should be able to edit the curve - just as you can edit colour styles. • Enable transform on the timeline. I should be able to flip or scale keyframes. I hope to continue trying more ideas. Craft focused tools have had to take a backseat in recent times. Pleasing to see this come to light.

On Figma Motion, my demo & review. This is one of those updates that clicked swiftly into my workflow. Like in my 3D pipeline, using this stick shift animation, I emphasize hardware feedback with motion from Figma Motion. Assets are animated in Fig Motion and loaded into Blender as video textures for mesh and material control. It's advantageous since it is built into an ubiquitous ecosystem. A lot of work these days start in Figma even for someone like me that works outside the tool frequently. Can't over-emphasize my appreciation for the schema used to introduce motion to nearly all properties. Reminds me of how motion works in Blender - with better complexity management. Better than introducing more menus for those. Ease curves iD between keyframes is also an improvement over existing systems. Having spent some time on the tool, here are updates I'll like to see to Figma Motion. These are based on my immediate needs as at the time of working on this demo: • Using modifier keys (CMD, Shift, Ctrl) to jump / nudge across the timeline. E.g: holding down Shift key to make the playhead jump 100ms etc. • Jump to previous or next keyframe. • Better anchor point trails • Renaming layers from the timeline as well. • Editable custom bezier styles: after saving a style, I should be able to edit the curve - just as you can edit colour styles. • Enable transform on the timeline. I should be able to flip or scale keyframes. I hope to continue trying more ideas. Craft focused tools have had to take a backseat in recent times. Pleasing to see this come to light.

seyi

28,871 görüntüleme • 25 gün önce

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 görüntüleme • 10 ay önce

🎬 Introducing MuseSteamer—an image-to-video model newly launched at Baidu AI Day. Here's what it offers: - Generation of synchronized visuals, Chinese dialogue, and sound effects from a single image. - Creation of 10-second, 1080P cinematic-quality clips with nuanced facial expressions and camera motion. - A model family that includes Turbo (available in beta now), Lite, Pro, and a full lineup of voiced versions. MuseSteamer is now integrated into Baidu Search. It's built to serve both everyday creators and professional film studios. Explore MuseSteamer:

🎬 Introducing MuseSteamer—an image-to-video model newly launched at Baidu AI Day. Here's what it offers: - Generation of synchronized visuals, Chinese dialogue, and sound effects from a single image. - Creation of 10-second, 1080P cinematic-quality clips with nuanced facial expressions and camera motion. - A model family that includes Turbo (available in beta now), Lite, Pro, and a full lineup of voiced versions. MuseSteamer is now integrated into Baidu Search. It's built to serve both everyday creators and professional film studios. Explore MuseSteamer:

Baidu Inc.

43,731 görüntüleme • 1 yıl önce

🌐 New website is now live! First up is the light mode, with dark mode coming soon. We're thrilled about what's next— ready to scream for these upcoming updates? 🌀 Brand New Roadmap (some product timelines may be adjusted based on development progress) 🌀 Kingnet AI V2 Official Release – A completely revamped dApp experience! Next week, we’ll officially open access to the Animation Generator and Agent, making interactions smarter and simpler than the first-gen version 🌀 Public Docs & GitHub Code Open-Source 🌀 Official Deployment on Solana & BNB Chain – Users will be able to pay on-chain to generate custom assets and export them (FBX format for direct download and use). TON integration will follow. 🌀$KNET’s First Utility Feature As always, we’ve included a surprise at the end of our video, showcasing the full workflow of AI text-to-3D model to in-game motion model: 1️⃣ Enter the Agent virtual assistant, use text prompts to generate a 3D character motion model. 2️⃣ The Agent generates a 3D base model, automatically bakes and saves textures. 3️⃣ Auto-rigging, joint adjustments, and natural motion generation. 4️⃣ Users can pay to download the FBX file with animations. 5️⃣ The file can be directly imported into Unity or Unreal Engine, with options to import animations, set animation cuts & loops—ready for in-game use! Continuous Delivery.

Kingnet AI

31,023 görüntüleme • 1 yıl önce