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We’ve prepared a new example showing how to create billboarding in your games! 🌲 It includes two modes: - always face the camera - like the smoke ☁️ - lock rotation to a vertical axis - trees and rocks🌲🪨 We combined here 3D models by Kay 🎨 and 2D...

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Defold Engine

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15,305 views • 5 months ago •via X (Twitter)

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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.
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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.

MrNeRF

52,849 views • 1 year ago

✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan
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✨ 3d models are now LIVE on Photo AI 😊 You can now turn any AI photo you make into a 3d model by pressing [ 📦 Make 3d model ] And then you can view it inside Photo AI or download it as a .GLB 3d model file It's still very early in AI generated 3d model world but it's nice to have this feature working already As always, the models will keep improving, so this feature will keep getting better (like it did with video, it sucked before, now it's getting passable) Next would be nice to switch to .USDZ so you can load it straight into your iPhone with ARKit and put it in your room Available now for everyone on the Premium and Ultra plan

@levelsio

112,930 views • 1 year ago

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.
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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.

Kling AI

34,303 views • 9 months ago

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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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:

Shikun Liu

22,471 views • 11 months ago

🌀 #Live3D #Live2D #Live2DWIP 🌀 Today, I’d like to share two of the three methods I use to create joint twisting in my Live2D-based 2D Pseudo-3D concept. On the right is the Glue Joint： The basic idea is to use Glue to connect two pseudo-3D ArtMeshes. By taking advantage of Glue’s weight settings, it can create an effect similar to a 3D joint. Once the weights are set up, it can move very freely with a relatively low amount of work. However, it also shares the same weakness as 3D joints: when rotated at large angles, it can produce unpleasant twisting and distortion. On the left is the Hand-Bent Joint： The basic idea is to add bending Keyforms directly to a Warp Deformer, then manually sculpt the ideal deformation. This method can create the most detailed and refined joints, depending entirely on how the rigger designs them. The downside is that it requires much more work. Every additional degree of freedom requires a significant amount of manual adjustment. These two joint methods have almost opposite characteristics, so in actual Live3D work, I choose the most suitable method depending on the situation. For example, in this hand model, I used Hand-Bent Joints for the arm twisting and fingers, while the wrist uses a Glue Joint.✨
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🌀 #Live3D #Live2D #Live2DWIP 🌀 Today, I’d like to share two of the three methods I use to create joint twisting in my Live2D-based 2D Pseudo-3D concept. On the right is the Glue Joint： The basic idea is to use Glue to connect two pseudo-3D ArtMeshes. By taking advantage of Glue’s weight settings, it can create an effect similar to a 3D joint. Once the weights are set up, it can move very freely with a relatively low amount of work. However, it also shares the same weakness as 3D joints: when rotated at large angles, it can produce unpleasant twisting and distortion. On the left is the Hand-Bent Joint： The basic idea is to add bending Keyforms directly to a Warp Deformer, then manually sculpt the ideal deformation. This method can create the most detailed and refined joints, depending entirely on how the rigger designs them. The downside is that it requires much more work. Every additional degree of freedom requires a significant amount of manual adjustment. These two joint methods have almost opposite characteristics, so in actual Live3D work, I choose the most suitable method depending on the situation. For example, in this hand model, I used Hand-Bent Joints for the arm twisting and fingers, while the wrist uses a Glue Joint.✨

📐Hephaestus📏Live2D匠人魂

18,894 views • 3 months ago

【🌷 3D REVEAL~! 🌷】 It was a dream (of spring hehe) come true to join some of my most precious friends to celebrate Nimi's 1-year #nimiversary at her 3D concert and to sing and dance together~! 🎶 This is actually both my new 3D model and was my first time doing mocap. 💖 It means a lot to me, and I'm so happy it got to be with friends!!! I love love love how Ayn @ VGEN was able to bring my wonderful mama さえきやひろ💀's art to life so skillfully and beautifully. 🎨 This is the reveal of my proper main hairstyle too~! I'm able to express myself so well with it, from every soft and silly expression, so the way the flowy dress moves like petals in the wind, to the bounce and flutter of my wings! 🎀 And this model actually was made possible BECAUSE of Nimi 💚 I didn't have a 3D model at all at the time, but I'm happy that she wanted me to be able to stand by her side and dance together and hug her ;////; I will treasure this model always. It's another important addition to the fairytale we've been writing together! I'll keep putting all of my heart into blooming more and more as an idol, and making happy memories with you all like we did today!
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【🌷 3D REVEAL~! 🌷】 It was a dream (of spring hehe) come true to join some of my most precious friends to celebrate Nimi's 1-year #nimiversary at her 3D concert and to sing and dance together~! 🎶 This is actually both my new 3D model and was my first time doing mocap. 💖 It means a lot to me, and I'm so happy it got to be with friends!!! I love love love how Ayn @ VGEN was able to bring my wonderful mama さえきやひろ💀's art to life so skillfully and beautifully. 🎨 This is the reveal of my proper main hairstyle too~! I'm able to express myself so well with it, from every soft and silly expression, so the way the flowy dress moves like petals in the wind, to the bounce and flutter of my wings! 🎀 And this model actually was made possible BECAUSE of Nimi 💚 I didn't have a 3D model at all at the time, but I'm happy that she wanted me to be able to stand by her side and dance together and hug her ;////; I will treasure this model always. It's another important addition to the fairytale we've been writing together! I'll keep putting all of my heart into blooming more and more as an idol, and making happy memories with you all like we did today!

Phoebe Chan 🐝🌸 Densetsu.EXE 🔜 Skyline Serenade

63,861 views • 7 months ago

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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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!

Anshu

29,342 views • 3 months ago

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch
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🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,666 views • 2 years ago

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below
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been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below

TechHalla

70,431 views • 6 months ago

🎮 𝐅𝐞𝐛𝐫𝐮𝐚𝐫𝐲 𝟐𝟎𝟐𝟓 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐔𝐩𝐝𝐚𝐭𝐞 🚨 Big updates across multiple areas of Varsity this month! • Coach Schemes in Dynasty Mode – Every coach now has a unique scheme and playstyle, influencing how your team plays. Do you stick with your staff and try new strategies, or hire a coach who specializes in your preferred system? • Audio & Settings Enhancements – A custom Varsity marching band drumline is being composed to add more energy to the game. Plus, menus now have sound effects, and new resolution options (16:9 and 16:10 ultrawide) have been added. • 3D Player & Stadium Models – The new player models are fully integrated, showcasing enhanced customization and animations. Pro-style stadiums for playoffs and championships are also in development! • Motion Capture Upgrades – Defensive back (DB) movements have been refined using motion capture for better realism. We’ve also improved base locomotion (running, turning, and movement) and are hiring a full-time AAA animation specialist. This month was all about refining core gameplay features and bringing more realism to Varsity. Stay tuned for more updates as we keep pushing towards launch! Support us on Patreon for Steam Pre-Alpha PlayTest access, and don’t forget to wish list on Steam! #Varsity #HSFootballVideoGame #DynastyMode #FootballGaming #HighSchoolFootball #CoachSchemes #3DModels #MotionCapture
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🎮 𝐅𝐞𝐛𝐫𝐮𝐚𝐫𝐲 𝟐𝟎𝟐𝟓 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐔𝐩𝐝𝐚𝐭𝐞 🚨 Big updates across multiple areas of Varsity this month! • Coach Schemes in Dynasty Mode – Every coach now has a unique scheme and playstyle, influencing how your team plays. Do you stick with your staff and try new strategies, or hire a coach who specializes in your preferred system? • Audio & Settings Enhancements – A custom Varsity marching band drumline is being composed to add more energy to the game. Plus, menus now have sound effects, and new resolution options (16:9 and 16:10 ultrawide) have been added. • 3D Player & Stadium Models – The new player models are fully integrated, showcasing enhanced customization and animations. Pro-style stadiums for playoffs and championships are also in development! • Motion Capture Upgrades – Defensive back (DB) movements have been refined using motion capture for better realism. We’ve also improved base locomotion (running, turning, and movement) and are hiring a full-time AAA animation specialist. This month was all about refining core gameplay features and bringing more realism to Varsity. Stay tuned for more updates as we keep pushing towards launch! Support us on Patreon for Steam Pre-Alpha PlayTest access, and don’t forget to wish list on Steam! #Varsity #HSFootballVideoGame #DynastyMode #FootballGaming #HighSchoolFootball #CoachSchemes #3DModels #MotionCapture

Varsity - High School Football Video Game

99,319 views • 1 year ago

I've seen a lot of animatics lately that are super detailed, essentially viewport previews of the final shot. But an animatic at it's core doesn't need to be anything fancy. Its main purpose is to plan and test the timing, camera angles, movement, and overall composition. With animatics you have to keep in mind the following: Pre-visualization: It allows to see a basic version of the animation or scene before committing to detailed work. Timing and pacing: An animatic helps identify how long each scene or shot should last, ensuring that the timing feels right. Planning: It helps with layout, camera angles, and transitions. By visualizing the shots, the team can ensure that the framing and overall design of the scenes work well in 3D space. Efficiency: It allows the team to test and fix any potential issues early on, like awkward movements or awkward pacing, before spending time on high-quality rendering or complex animation. In short, an animatic helps in conceptualizing the final animation by giving a low-res, rough version of the scenes, which guides the entire production process in terms of visual design, timing, and storytelling.
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I've seen a lot of animatics lately that are super detailed, essentially viewport previews of the final shot. But an animatic at it's core doesn't need to be anything fancy. Its main purpose is to plan and test the timing, camera angles, movement, and overall composition. With animatics you have to keep in mind the following: Pre-visualization: It allows to see a basic version of the animation or scene before committing to detailed work. Timing and pacing: An animatic helps identify how long each scene or shot should last, ensuring that the timing feels right. Planning: It helps with layout, camera angles, and transitions. By visualizing the shots, the team can ensure that the framing and overall design of the scenes work well in 3D space. Efficiency: It allows the team to test and fix any potential issues early on, like awkward movements or awkward pacing, before spending time on high-quality rendering or complex animation. In short, an animatic helps in conceptualizing the final animation by giving a low-res, rough version of the scenes, which guides the entire production process in terms of visual design, timing, and storytelling.

Voxyde

22,244 views • 1 year ago

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇
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How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇

Ridark

11,592 views • 1 month ago

If you think OpenAI Sora is a creative toy like DALLE, ... think again. Sora is a data-driven physics engine. It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, "intuitive" physics, long-horizon reasoning, and semantic grounding, all by some denoising and gradient maths. I won't be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be! Let's breakdown the following video. Prompt: "Photorealistic closeup video of two pirate ships battling each other as they sail inside a cup of coffee." - The simulator instantiates two exquisite 3D assets: pirate ships with different decorations. Sora has to solve text-to-3D implicitly in its latent space. - The 3D objects are consistently animated as they sail and avoid each other's paths. - Fluid dynamics of the coffee, even the foams that form around the ships. Fluid simulation is an entire sub-field of computer graphics, which traditionally requires very complex algorithms and equations. - Photorealism, almost like rendering with raytracing. - The simulator takes into account the small size of the cup compared to oceans, and applies tilt-shift photography to give a "minuscule" vibe. - The semantics of the scene does not exist in the real world, but the engine still implements the correct physical rules that we expect. Next up: add more modalities and conditioning, then we have a full data-driven UE that will replace all the hand-engineered graphics pipelines.
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If you think OpenAI Sora is a creative toy like DALLE, ... think again. Sora is a data-driven physics engine. It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, "intuitive" physics, long-horizon reasoning, and semantic grounding, all by some denoising and gradient maths. I won't be surprised if Sora is trained on lots of synthetic data using Unreal Engine 5. It has to be! Let's breakdown the following video. Prompt: "Photorealistic closeup video of two pirate ships battling each other as they sail inside a cup of coffee." - The simulator instantiates two exquisite 3D assets: pirate ships with different decorations. Sora has to solve text-to-3D implicitly in its latent space. - The 3D objects are consistently animated as they sail and avoid each other's paths. - Fluid dynamics of the coffee, even the foams that form around the ships. Fluid simulation is an entire sub-field of computer graphics, which traditionally requires very complex algorithms and equations. - Photorealism, almost like rendering with raytracing. - The simulator takes into account the small size of the cup compared to oceans, and applies tilt-shift photography to give a "minuscule" vibe. - The semantics of the scene does not exist in the real world, but the engine still implements the correct physical rules that we expect. Next up: add more modalities and conditioning, then we have a full data-driven UE that will replace all the hand-engineered graphics pipelines.

Jim Fan

6,183,508 views • 2 years ago

it's a big day for vortex generators. vortex generator generators, in particular Sam Altman do you remember talking about fire tornadoes? similar. i should show you and olly some relevant things on that at lightcell energy 🔆 we 3d printed these ceramic compound nozzles to be a steady rotation where the air and fuel are injected and merge smoothly, like a combined swirl nozzle and laminar flow element. it works weirdly well and makes a steady flow, and, channels and accelerates exhaust along the axis. it is very familiar to OpenAI's flow field, and i'm curious how similar!! and if such a flow field might be forced, and thus, exploited, in some actually physical invention. it doesn't seem like this basic idea of pulling the flow up the center, to do axial strengthing. and spiral nozzles, requires the continuum hypothesis to do vorticity amplification / concentration like this. so it should work in a device. similar high velocity axial vorticity amplification. in addition to high speed rotation stabilizing a density gradient like a flame column, it stablizes kinetically, in free air, and can be used for gas separation, particle extraction and energy pumping, like in a vortex tube (Ranque Hilsch vortex tubes at high and low temp ) and otherwise. not sure these are just my first thoughts seeing the paper your employee and my friend Abe Fetterman suggested double ended, which is the next thing we gotta try!
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it's a big day for vortex generators. vortex generator generators, in particular Sam Altman do you remember talking about fire tornadoes? similar. i should show you and olly some relevant things on that at lightcell energy 🔆 we 3d printed these ceramic compound nozzles to be a steady rotation where the air and fuel are injected and merge smoothly, like a combined swirl nozzle and laminar flow element. it works weirdly well and makes a steady flow, and, channels and accelerates exhaust along the axis. it is very familiar to OpenAI's flow field, and i'm curious how similar!! and if such a flow field might be forced, and thus, exploited, in some actually physical invention. it doesn't seem like this basic idea of pulling the flow up the center, to do axial strengthing. and spiral nozzles, requires the continuum hypothesis to do vorticity amplification / concentration like this. so it should work in a device. similar high velocity axial vorticity amplification. in addition to high speed rotation stabilizing a density gradient like a flame column, it stablizes kinetically, in free air, and can be used for gas separation, particle extraction and energy pumping, like in a vortex tube (Ranque Hilsch vortex tubes at high and low temp ) and otherwise. not sure these are just my first thoughts seeing the paper your employee and my friend Abe Fetterman suggested double ended, which is the next thing we gotta try!

Danielle Fong 🔆

14,746 views • 13 days ago

I’ve been craving a video editor with motion baked in, designed to work with my own AI agents. Claude, Codex, whatever I want to use. I don’t want to be blocked by someone else’s credit system for every little interaction. I also wanted a Figma-like canvas/editor for when I want to jump in and tweak any little detail. So we built it: Video & audio editing: cut, zoom, speed up, add B-roll. Motion, animation, 3D, custom shaders & effects, custom 3D models, dynamic components & templates, audio and beat matching. And it’s all programmable. Design your own assets directly on the canvas, bring them in from Figma or the web, or just let the agent find them, mock them up, or record what it needs automatically. You can even drop in your screen recordings and just let loose. Supercut users: yes, it’ll have first-class support for your recordings, but it’ll work with anything. I’m going to be dropping a lot more examples and tutorials, so follow along. DM me for early access if you’re willing to give feedback. We’ll open it up to everyone very soon.
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I’ve been craving a video editor with motion baked in, designed to work with my own AI agents. Claude, Codex, whatever I want to use. I don’t want to be blocked by someone else’s credit system for every little interaction. I also wanted a Figma-like canvas/editor for when I want to jump in and tweak any little detail. So we built it: Video & audio editing: cut, zoom, speed up, add B-roll. Motion, animation, 3D, custom shaders & effects, custom 3D models, dynamic components & templates, audio and beat matching. And it’s all programmable. Design your own assets directly on the canvas, bring them in from Figma or the web, or just let the agent find them, mock them up, or record what it needs automatically. You can even drop in your screen recordings and just let loose. Supercut users: yes, it’ll have first-class support for your recordings, but it’ll work with anything. I’m going to be dropping a lot more examples and tutorials, so follow along. DM me for early access if you’re willing to give feedback. We’ll open it up to everyone very soon.

Neil

23,042 views • 1 month ago

Cross chain fun, and a great example! 🔊 The BSB memecoin launched a week ago on Base and is now a community run project. Today our partners at VIPE produced this open mint avatar which is shown here inside the live Pavia Playground, in a house built by the amazing Mohamad Mneimneh for NIDO, who ran the main stage at the Pavia Plaza live event. The track playing in this clip was made by Aquatic Cat Dolphin. Projects: Our powerful Pavia Studio (builder tool) is free to use, you can import 3D assets and publish to the live Pavia Playground. As well as a range of free Ready Player Me avatars, Vipe can build you custom project avatar/s which we can integrate. Wire in some awesome music (two artists listed above) and boom! you have some amazing content and place for your crew to meet up and hang out. For those looking for a permanent home, land plots are available on secondary markets, single plots and larger 3x3 estates. We have loads coming such as land merging, Pavia Flux, NFT bridges, scaling tests and much more! [Note. cryptoassets, especially memecoins, are high risk and extremely volatile, please always DYOR]
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Cross chain fun, and a great example! 🔊 The BSB memecoin launched a week ago on Base and is now a community run project. Today our partners at VIPE produced this open mint avatar which is shown here inside the live Pavia Playground, in a house built by the amazing Mohamad Mneimneh for NIDO, who ran the main stage at the Pavia Plaza live event. The track playing in this clip was made by Aquatic Cat Dolphin. Projects: Our powerful Pavia Studio (builder tool) is free to use, you can import 3D assets and publish to the live Pavia Playground. As well as a range of free Ready Player Me avatars, Vipe can build you custom project avatar/s which we can integrate. Wire in some awesome music (two artists listed above) and boom! you have some amazing content and place for your crew to meet up and hang out. For those looking for a permanent home, land plots are available on secondary markets, single plots and larger 3x3 estates. We have loads coming such as land merging, Pavia Flux, NFT bridges, scaling tests and much more! [Note. cryptoassets, especially memecoins, are high risk and extremely volatile, please always DYOR]

Pavia

30,154 views • 2 years ago

🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.
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🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.

TheNewPhysics

347,458 views • 3 months ago

Phase Shift Initiated Since before GTC 2024, NVIDIA GDN (Graphics Delivery Network) has been a strong catalyst for the enthusiasm we have seen for our innovation, not only among the community but also among the team. NVIDIA’s technology, platforms, and teams have consistently inspired us - with GDN being no exception. Recently, we’ve recalibrated our development efforts, doubling down on bringing our release to GDN’s cutting-edge infrastructure. Five of our developers are now fully focused on GDN integration, and in this week alone, we’ve achieved four major backend milestones, and are quickly closing in on three more. These advancements are propelling Web3 technology directly onto NVIDIA GeForce Servers. By harnessing NVIDIA GDN platform, we’re transforming high-fidelity 3D content into a seamless Web3 experience accessible anywhere—directly in your browser. No downloads. No accounts. Just Blockchain. This breakthrough eliminates the reliance on high-end hardware, redefining accessibility for industries like gaming, manufacturing, and media. With Kondux and GDN, even the most resource-intensive 3D applications can be effortlessly streamed to any device, delivering unmatched performance and interactivity. We’re not just overcoming barriers; we’re creating an entirely new playground for high-fidelity 3D assets.
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Phase Shift Initiated Since before GTC 2024, NVIDIA GDN (Graphics Delivery Network) has been a strong catalyst for the enthusiasm we have seen for our innovation, not only among the community but also among the team. NVIDIA’s technology, platforms, and teams have consistently inspired us - with GDN being no exception. Recently, we’ve recalibrated our development efforts, doubling down on bringing our release to GDN’s cutting-edge infrastructure. Five of our developers are now fully focused on GDN integration, and in this week alone, we’ve achieved four major backend milestones, and are quickly closing in on three more. These advancements are propelling Web3 technology directly onto NVIDIA GeForce Servers. By harnessing NVIDIA GDN platform, we’re transforming high-fidelity 3D content into a seamless Web3 experience accessible anywhere—directly in your browser. No downloads. No accounts. Just Blockchain. This breakthrough eliminates the reliance on high-end hardware, redefining accessibility for industries like gaming, manufacturing, and media. With Kondux and GDN, even the most resource-intensive 3D applications can be effortlessly streamed to any device, delivering unmatched performance and interactivity. We’re not just overcoming barriers; we’re creating an entirely new playground for high-fidelity 3D assets.

Kondux

96,089 views • 1 year ago