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Bai et al., "Positional Encoding Field" Make your RoPE encoding 3D by including a z axis, then manipulate your image by simply manipulating your positional encoding in 3D --> novel view synthesis. Neat idea.

Kwang Moo Yi

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47,086 görüntüleme • 11 ay önce •via X (Twitter)

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one 2d photo --> 3d gaussian splat quick test with Echo-2 by SpAItial -- these 3d scene generation models are getting better! already at a sufficient quality to serve as a virtual set / backdrop in your 3d tool of choice

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Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

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Combining the explicit control of 3D software with the creativity of generative AI models is a promising yet underrated workflow. Build your 3D scenes procedurally by describing them in natural language, then take them all the way with your image & video models of choice. Tools like intangible are built around such a workflow so you don't need to duct-tape apps together. Pretty cool!

Combining the explicit control of 3D software with the creativity of generative AI models is a promising yet underrated workflow. Build your 3D scenes procedurally by describing them in natural language, then take them all the way with your image & video models of choice. Tools like intangible are built around such a workflow so you don't need to duct-tape apps together. Pretty cool!

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@levelsio

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If you typically stream with a 3D model, I highly recommend that you pose your model when you’re not doing full body mocap instead of letting it stay in the stiff generic pose! It makes a huge difference in how your energy is conveyed (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ I’m only saying this because I noticed this a lot with my own 3D kids, but here’s a comparison showcase: ← default pose & default pendulum physics in Warudo → custom poses & slightly adjusted pendulum physics If you find it hard to pose within Warudo by using bone offsets or none of the existing poses in Warudo vibe with you, you can make your own like me! I made my custom poses in “VRM Posing Desktop” (this is the BEST vrm posing app I’ve ever used in the past 3 years) and exported them as Unity anims then dropped them into Warudo’s animation folder! You can then make a simple blueprint in Warudo to toggle between poses and make your model look more alive! It should fit especially well for just chatting streams 🙂‍↕️✨

𝗞𝗔𝗥𝗜𝗛𝗔 🌘🍀 ᴄᴏᴍᴍꜱ ᴏᴘᴇɴ! ☻

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How to generate 3D miniature city models and animate them using Kling AI? This visual effect can be created with Kling O1, and then rotated in 3D using Image to Video. The image prompt used for generation is as follows: Present a clear, 45° top-down isometric miniature 3D cartoon scene of New York featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood.Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title "New York" in large bold text in white. You can create different effects by changing the city name according to your needs.

How to generate 3D miniature city models and animate them using Kling AI? This visual effect can be created with Kling O1, and then rotated in 3D using Image to Video. The image prompt used for generation is as follows: Present a clear, 45° top-down isometric miniature 3D cartoon scene of New York featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood.Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title "New York" in large bold text in white. You can create different effects by changing the city name according to your needs.

Kling AI

34,303 görüntüleme • 9 ay önce

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

Everyone's sleeping on image-to-3D AI models. They can make your app look incredibly unique, with just a little effort. Here's how. This is my calorie tracker, built in a week with nothing but prompting. Just Claude Code + a couple APIs. The visuals are all AI-generated. I'll be sharing the full workflow + all the crazy technical stuff Claude and I did to make this work, so nobody has to struggle through it like me. Deep dive coming soon! Till then, this is the high-level idea: 1. Get a clean image of the food (or whatever your asset is) - In my app, the user describes foods via text, or attaches images (or both) - If text, an LLM extracts the food description and formats it into a specific prompt I tuned for this design, and we generate an image using Z-Image Turbo through fal - If image, we do the same thing but with FLUX.2 [dev] to edit the user image into our reference design - Originally, both used Google Nano Banana, but switching to open models cut costs and latency a ton 2. Gaussian splatting (2D image → 3D model) - I tried various 2D-to-3D options on fal and ended up with TripoSplat as my preferred balance of speed, cost, latency; this turns an image into a 3D model that looks super high quality (link below) - The app displays the 2D image while our backend generates the 3D splat - We "groom" the splat to reduce size and load time by culling low-opacity/scale points 3. Render efficiently on device Originally, it looked great but ran at 10 FPS. Getting to 120 FPS was a crazy journey. TL;DR: - SwiftUI had to go; it forced us to render each asset in independent MTKViews, which wasn't workable - Instead, we composite every dish into one full-bleed CAMetalLayer using MetalSplatter (link below) - We had to make some optimizations within MetalSplatter's code too, to reduce the overhead of sorting points per render Then I added some finishing touches like the subtle rotation and parallax as they move around. I think it turned out pretty cool :) Overall, this took some effort, but we still got it done in less than a day. Hopefully your agent can follow in the footsteps of mine and do it much faster. Keep an eye out for the bigger writeup, which'll give your agent everything it needs. If you have any questions, drop em below!

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Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

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🌍 As some of you might know, last year we started building an app that required a 3D Map, and we were taken aback by the lack of good SDKs. They’re all clunky, slow, and unstable.🤔 Today, we're thrilled to introduce Cartes - a fast, easy-to-use, and visually appealing 3D Map SDK for Unity. This tool is built to empower your creativity in developing delightful apps and games for the Real World Metaverse. 📱🤳 ⚡️ With blazing-fast performance, our SDK offers a seamless integration for your (modern) Unity projects. We provide default navigation features, intuitive gestures, and clustering capabilities that we meticulously refined over hundreds of hours and proof-tested in guerilla tests. 🏞️ We wanted to build a genuinely 3D map, with 3D terrain, monuments and decorations, able to transition smoothly from a global view down to the human eye level. 🔍 #Unity #AR #Maps #LBE

Tina Debove ᯅ

15,215 görüntüleme • 3 yıl önce

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more

✨ I can now generate 3d assets for my drone sim at directly from Cursor (sponsor of #vibejam) I need buildings that you'd see in a war torn city, like warehouses in ruins, broken down abandoned houses, bombed out bridges etc. Nano Banana Pro or 2 can generate them really well and then you can put them in an image-to-3d model and you get a GLB or FBX That one you can then import into your Three.js game, the models might be big though, in my case like 16MB, so I ask it to compress it and make it more low poly so it loads fast ThreeJS then loads the individual GLBs on page load and puts them in my drone sim somewhere randomly, I think I should remove some of the grass and match the sandy color of the ruins though to make it fit in more

@levelsio

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2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!

2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!

@levelsio

584,511 görüntüleme • 1 yıl önce

Bug reporting is an essential part of making sure Discord is up and running the way it's supposed to. Notice a glitch that's getting in the way of your chatting experience? Make sure to submit a bug report to our team by including: - A description of the bug - Steps to reproduce - Expected results - Actual results - Discord client info - Debug logs - An image/video attachment of what you're seeing Learn more about bug reporting:

Bug reporting is an essential part of making sure Discord is up and running the way it's supposed to. Notice a glitch that's getting in the way of your chatting experience? Make sure to submit a bug report to our team by including: - A description of the bug - Steps to reproduce - Expected results - Actual results - Discord client info - Debug logs - An image/video attachment of what you're seeing Learn more about bug reporting:

Discord Support

37,306 görüntüleme • 1 yıl önce

I created a dance movement sheet by using a reference image to animate every 16 panels from the reference image I provided. GPT Image 2 + See Dance 2.0 on Yapper Tutorial Below Prompt Here’s every step with the text under each heading: 1. Basic Stance Stand with your feet shoulder-width apart. Relax your knees. Keep your upper body relaxed. Get ready. 2. Step to the Right Step to the right. Move your body in the direction of the step. Keep your knees soft. Keep your gaze forward. 3. Step to the Left Step to the left. Move your body in the direction of the step. Keep your knees soft. Return to the starting position. 4. Two Steps Take two steps in sequence. Step to the right first. Then step to the left. Connect smoothly. 5. Body Wave Start the wave from your chest. Move through your ribs and hips. Finish with your lower body. Make the motion smooth like a wave. 6. Hip Sway Move your hips side to side. Shift your weight with the motion. Use your body naturally. Keep your upper body relaxed. 7. Arm Swing Swing your arms wide. Step to beat with your feet. Coordinate your arms and steps. Return your arms to center. 8. Turn Preparation Prepare for a turn in place. Cross one foot over the other. Use your core to maintain balance. Spot in the direction of the turn. 9. Right Turn Turn to the right. Keep your core engaged. Pivot on the ball of your foot. Spot forward after the turn. 10. Left Turn Turn to the left. Keep your core engaged. Pivot quickly. Keep your posture upright. 11. Jump Up Bend your knees and jump up. Reach your arms overhead. Land softly on your feet. Connect to the next move. 12. Kick Pose Kick your leg forward. Keep your supporting leg stable. Engage your core for balance. Control your landing. 13. Side Lunge Step wide to one side. Bend one knee and lower your body. Keep the other leg straight. Show the direction of your strength. 14. Freeze Pose Hit a strong pose on the beat. Freeze your body momentarily. Create a powerful shape. Show your presence. 15. Finishing Pose Finish the dance gracefully. Keep your balance. Show your confidence. End with a clean, sharp pose. 16. Whole Flow Connect from the basic stance to the finishing pose. The steps, waves, and turns flow naturally together. Express your own style.

I created a dance movement sheet by using a reference image to animate every 16 panels from the reference image I provided. GPT Image 2 + See Dance 2.0 on Yapper Tutorial Below Prompt Here’s every step with the text under each heading: 1. Basic Stance Stand with your feet shoulder-width apart. Relax your knees. Keep your upper body relaxed. Get ready. 2. Step to the Right Step to the right. Move your body in the direction of the step. Keep your knees soft. Keep your gaze forward. 3. Step to the Left Step to the left. Move your body in the direction of the step. Keep your knees soft. Return to the starting position. 4. Two Steps Take two steps in sequence. Step to the right first. Then step to the left. Connect smoothly. 5. Body Wave Start the wave from your chest. Move through your ribs and hips. Finish with your lower body. Make the motion smooth like a wave. 6. Hip Sway Move your hips side to side. Shift your weight with the motion. Use your body naturally. Keep your upper body relaxed. 7. Arm Swing Swing your arms wide. Step to beat with your feet. Coordinate your arms and steps. Return your arms to center. 8. Turn Preparation Prepare for a turn in place. Cross one foot over the other. Use your core to maintain balance. Spot in the direction of the turn. 9. Right Turn Turn to the right. Keep your core engaged. Pivot on the ball of your foot. Spot forward after the turn. 10. Left Turn Turn to the left. Keep your core engaged. Pivot quickly. Keep your posture upright. 11. Jump Up Bend your knees and jump up. Reach your arms overhead. Land softly on your feet. Connect to the next move. 12. Kick Pose Kick your leg forward. Keep your supporting leg stable. Engage your core for balance. Control your landing. 13. Side Lunge Step wide to one side. Bend one knee and lower your body. Keep the other leg straight. Show the direction of your strength. 14. Freeze Pose Hit a strong pose on the beat. Freeze your body momentarily. Create a powerful shape. Show your presence. 15. Finishing Pose Finish the dance gracefully. Keep your balance. Show your confidence. End with a clean, sharp pose. 16. Whole Flow Connect from the basic stance to the finishing pose. The steps, waves, and turns flow naturally together. Express your own style.

Sharon Riley

54,571 görüntüleme • 4 ay önce

We asked the universe if this was a good idea. It said "probably” FROM ONE WORLD TO ANOTHER - a collab between The Del Mundos & OpenSea celebrating the mission to connect the Digital and Real Worlds Mint: August 5th Proceeds to CLIMETA, a treasury for nature 👇 A collection of four animated collectable artworks demonstrating how digital creativity can make a positive real-world impact Created by digital artist Dspall (dspall ⭕ ) - a 3D Artist and digital nomad. Working at the intersection of AI and traditional 3D pipelines to create surreal worlds with a quiet sense of humour, often bridging the real and the virtual The NFT’s are accessible to everyone and available in an open edition mint Edition of four - choose your favourite or collect all! Alerts on - Minting on August 5th More news to follow including incoming Spaces 👀 People Powered Positivity ✊

We asked the universe if this was a good idea. It said "probably” FROM ONE WORLD TO ANOTHER - a collab between The Del Mundos & OpenSea celebrating the mission to connect the Digital and Real Worlds Mint: August 5th Proceeds to CLIMETA, a treasury for nature 👇 A collection of four animated collectable artworks demonstrating how digital creativity can make a positive real-world impact Created by digital artist Dspall (dspall ⭕ ) - a 3D Artist and digital nomad. Working at the intersection of AI and traditional 3D pipelines to create surreal worlds with a quiet sense of humour, often bridging the real and the virtual The NFT’s are accessible to everyone and available in an open edition mint Edition of four - choose your favourite or collect all! Alerts on - Minting on August 5th More news to follow including incoming Spaces 👀 People Powered Positivity ✊

The Del Mundos

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