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From 2 second (faster on higher end GPUs) Gaussian splat generations, using Apple's "Sharp Monocular View Synthesis in Less Than a Second" code, running locally on my system. Imported into Octane Render 2026 which has fully path traced rendering of Gaussian splats. Stuff like this is why I've been... show more

Martin Nebelong

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36,774 görüntüleme • 7 ay önce •via X (Twitter)

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A few environments, created locally on my PC in a few seconds each using that cool new Apple image to Gaussian splat tech, stitched together and lit in Octane Render 2026 and combined with a few models made using Hyper3D by Deemos Rodin. Next year is bound to be the year of Generative 3d.

A few environments, created locally on my PC in a few seconds each using that cool new Apple image to Gaussian splat tech, stitched together and lit in Octane Render 2026 and combined with a few models made using Hyper3D by Deemos Rodin. Next year is bound to be the year of Generative 3d.

Martin Nebelong

10,485 görüntüleme • 7 ay önce

From 2D video to a very rough 4D Gaussian Splatting POC on the Apple Vision Pro 👀 Took Big Buck Bunny frames, turned them into Gaussian splats with Sharp (Apple), compressed them with PlayCanvas splat transform to SOG, and played all frames via WebXR + spark Still rough and not fully optimized, but wow, dynamic GS in AVP feels wild 🤯 Second part of the video is on my laptop #threejs

From 2D video to a very rough 4D Gaussian Splatting POC on the Apple Vision Pro 👀 Took Big Buck Bunny frames, turned them into Gaussian splats with Sharp (Apple), compressed them with PlayCanvas splat transform to SOG, and played all frames via WebXR + spark Still rough and not fully optimized, but wow, dynamic GS in AVP feels wild 🤯 Second part of the video is on my laptop #threejs

Stijn Spanhove

20,127 görüntüleme • 7 ay önce

New paper from Apple - Sharp Monocular View Synthesis in Less than a Second Mescheder et al. @ Apple just released a very impressive paper (congrats! 🎉🥳). You give it an image and it generates a really great looking 3d Gaussian representation. Uses depth pro. It's really good. The model is about 3GB, and takes ~5-10s on my M1 Max per image. Single feedforward pass network. The video is using "Metal Splatter" to view the .ply from ml-sharp on Apple Vision Pro. Some really wow moments today trying it on different scenes.

New paper from Apple - Sharp Monocular View Synthesis in Less than a Second Mescheder et al. @ Apple just released a very impressive paper (congrats! 🎉🥳). You give it an image and it generates a really great looking 3d Gaussian representation. Uses depth pro. It's really good. The model is about 3GB, and takes ~5-10s on my M1 Max per image. Single feedforward pass network. The video is using "Metal Splatter" to view the .ply from ml-sharp on Apple Vision Pro. Some really wow moments today trying it on different scenes.

Tim Davison ᯅ

132,865 görüntüleme • 7 ay önce

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu

Bilawal Sidhu

337,090 görüntüleme • 2 yıl önce

$Spatial AI is increasingly important, and the newest papers from #NVIDIAResearch, 3DGRT and 3DGUT, represent significant advancements in enabling researchers and developers to explore and innovate with 3D Gaussian Splatting techniques. 💎 3DGRT (Gaussian Ray Tracing) ➡️ - Enables secondary lighting effects such as refractions, shadows, depth of field, and mirrors. - Provides a foundation for further research in ray tracing-based rendering of Gaussian representations. - Achieves real-time rendering performance on modern GPUs. ✨ 3DGUT (Gaussain Unscented Transform) ➡️ - Support for distorted camera models and time-dependent effects like rolling shutter. - Enables phenomena such as reflections and refractions while retaining real-time efficiency. 📥 Code:$

Spatial AI is increasingly important, and the newest papers from #NVIDIAResearch, 3DGRT and 3DGUT, represent significant advancements in enabling researchers and developers to explore and innovate with 3D Gaussian Splatting techniques. 💎 3DGRT (Gaussian Ray Tracing) ➡️ - Enables secondary lighting effects such as refractions, shadows, depth of field, and mirrors. - Provides a foundation for further research in ray tracing-based rendering of Gaussian representations. - Achieves real-time rendering performance on modern GPUs. ✨ 3DGUT (Gaussain Unscented Transform) ➡️ - Support for distorted camera models and time-dependent effects like rolling shutter. - Enables phenomena such as reflections and refractions while retaining real-time efficiency. 📥 Code:

NVIDIA AI Developer

27,760 görüntüleme • 1 yıl önce

Gaussian Splatting is pretty wild. Being able to take dense collections of assets and package them into fast, real-time splats opens up a ton of new possibilities for look-dev, previs, and interactive scenes. For this test, I built a pile of 400 skulls - roughly 40+ million polygons (and over 400 8K texture instances) - rendered out a 600 frame dataset on The Render Network with OTOY OctaneRender and then converted the result into a single splat asset. Now it runs in real-time as one compact Gaussian Splat.

Gaussian Splatting is pretty wild. Being able to take dense collections of assets and package them into fast, real-time splats opens up a ton of new possibilities for look-dev, previs, and interactive scenes. For this test, I built a pile of 400 skulls - roughly 40+ million polygons (and over 400 8K texture instances) - rendered out a 600 frame dataset on The Render Network with OTOY OctaneRender and then converted the result into a single splat asset. Now it runs in real-time as one compact Gaussian Splat.

Spenser Dickerson

46,560 görüntüleme • 8 ay önce

🚀 Just created something completely new! I Rewrote Apple's Sharp to generate 3D Gaussian Splats from a single 360° equirectangular image made from FLUX 2 (Flex) One equirectangular image → Full spherical 3DGS world! ✨ New web interface - drag & drop your pano, select quality, download PLY ⚡ 2-3 sec on a older laptop Quality tiers: ▫️ Low: 500K splats ▫️ Medium: 2M splats ▫️ High: 4.7M splats ▫️ Ultra: 8.4M splats The pipeline: 360° depth estimation from equirectangular input Spherical projection maps pixels → 3D points Each point becomes a Gaussian with depth-scaled radius - Export to standard PLY for any 3DGS viewer (Unity) No multi-view capture. No COLMAP. No NeRF training. Just upload a pano, pick quality, get your splats. #3DGS #GaussianSplatting #ComputerVision #AI #madewithunity #Splats Sharp Monocular View Synthesis in Less Than a Second

🚀 Just created something completely new! I Rewrote Apple's Sharp to generate 3D Gaussian Splats from a single 360° equirectangular image made from FLUX 2 (Flex) One equirectangular image → Full spherical 3DGS world! ✨ New web interface - drag & drop your pano, select quality, download PLY ⚡ 2-3 sec on a older laptop Quality tiers: ▫️ Low: 500K splats ▫️ Medium: 2M splats ▫️ High: 4.7M splats ▫️ Ultra: 8.4M splats The pipeline: 360° depth estimation from equirectangular input Spherical projection maps pixels → 3D points Each point becomes a Gaussian with depth-scaled radius - Export to standard PLY for any 3DGS viewer (Unity) No multi-view capture. No COLMAP. No NeRF training. Just upload a pano, pick quality, get your splats. #3DGS #GaussianSplatting #ComputerVision #AI #madewithunity #Splats Sharp Monocular View Synthesis in Less Than a Second

Daniel Skaale

73,120 görüntüleme • 7 ay önce

What happens if you try to push and squash a Gaussian splat? :) Been playing with adding simplified deformation and a lightweight physics simulation layer to Gaussian splats, running directly in real time on mobile. Instead of simulating every splat, this uses a small CPU lattice to drive the deformation, plus a per-splat transform so they actually stretch and compress properly. No mesh conversion, no retraining. Just layered on top of the renderer. You can push, bend, squash a scan and it holds together surprisingly well... Still early, but I can see the potential in making splats feel more real, from static captures to something more tactile, something you can touch, throw, bounce around… or well just fold and squash on the floor :) #GaussianSplatting #3DGS #Unity

What happens if you try to push and squash a Gaussian splat? :) Been playing with adding simplified deformation and a lightweight physics simulation layer to Gaussian splats, running directly in real time on mobile. Instead of simulating every splat, this uses a small CPU lattice to drive the deformation, plus a per-splat transform so they actually stretch and compress properly. No mesh conversion, no retraining. Just layered on top of the renderer. You can push, bend, squash a scan and it holds together surprisingly well... Still early, but I can see the potential in making splats feel more real, from static captures to something more tactile, something you can touch, throw, bounce around… or well just fold and squash on the floor :) #GaussianSplatting #3DGS #Unity

𝐑𝐔𝐁𝐄𝐍🥽𝐅𝐑𝐎

51,154 görüntüleme • 3 ay önce

This is the power of VFX and AI now.. Creating volumetric effects in AI and then using AfterEffects and Premiere to layer and edit + Topaz Labs to upscale, you can visually achieve the same effects that would have taken me A LOT longer in Cinema4D/Blender. This is really amazing, only because if I can do this now with what we have, imagine where it will be in 6-mo 🔥 it will only get better. Soon we’ll be able to do a lot more within 3D environments with gaussian splats and volumetric effects. It won’t be long after that before Cinema, blender and others integrate gaussian splats and generative prompting directly into the software design and render process. At that point things are going to get REALLY BADASS! I’m chillin tonight, finally a night off lol. But tomorrow I’ll show you guys how I did this ❤️

This is the power of VFX and AI now.. Creating volumetric effects in AI and then using AfterEffects and Premiere to layer and edit + Topaz Labs to upscale, you can visually achieve the same effects that would have taken me A LOT longer in Cinema4D/Blender. This is really amazing, only because if I can do this now with what we have, imagine where it will be in 6-mo 🔥 it will only get better. Soon we’ll be able to do a lot more within 3D environments with gaussian splats and volumetric effects. It won’t be long after that before Cinema, blender and others integrate gaussian splats and generative prompting directly into the software design and render process. At that point things are going to get REALLY BADASS! I’m chillin tonight, finally a night off lol. But tomorrow I’ll show you guys how I did this ❤️

Dustin Hollywood

19,760 görüntüleme • 8 ay önce

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

MrNeRF

17,786 görüntüleme • 1 yıl önce

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Martin Nebelong

280,005 görüntüleme • 1 yıl önce

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.

Drivable 3D Gaussian Avatars paper page: present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable model for human bodies rendered with Gaussian splats. Current photorealistic drivable avatars require either accurate 3D registrations during training, dense input images during testing, or both. The ones based on neural radiance fields also tend to be prohibitively slow for telepresence applications. This work uses the recently presented 3D Gaussian Splatting (3DGS) technique to render realistic humans at real-time framerates, using dense calibrated multi-view videos as input. To deform those primitives, we depart from the commonly used point deformation method of linear blend skinning (LBS) and use a classic volumetric deformation method: cage deformations. Given their smaller size, we drive these deformations with joint angles and keypoints, which are more suitable for communication applications. Our experiments on nine subjects with varied body shapes, clothes, and motions obtain higher-quality results than state-of-the-art methods when using the same training and test data.

AK

327,105 görüntüleme • 2 yıl önce

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

Shruti

19,832 görüntüleme • 1 ay önce

I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.

I really want to show off the cutting edge tech the team is building and how we're turning AI image generation into real living environments using gaussian splats and at the :19 mark of this video you can see how we're blending splats together to create a real world. What this looks like from a gaming perspective is this all happens behind the players view to simulate a real world environment. As you move through the space, distance becomes a variable to consider on how images update and are experienced. We are creating a new reality in real time.

𝔹𝕃𝕍ℂ𝕂𝕃!𝔾ℍ𝕋

25,084 görüntüleme • 1 ay önce

$FAU Erlangen-Nürnberg presents TRIPS Trilinear Point Splatting for Real-Time Radiance Field Rendering paper page: Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.$

FAU Erlangen-Nürnberg presents TRIPS Trilinear Point Splatting for Real-Time Radiance Field Rendering paper page: Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.

AK

45,459 görüntüleme • 2 yıl önce

I want to be really honest. The tweet from Opensea means more than just a text to me. This is a reminder that’s I’m on the right path, and I put my blood and tears to get into position where I’m today. I live in a warzone, I see crazy stuff everyday out of my window for the past 3,5 years. I draw stuff while drones literally exploding on the background. You would ask why I’m not leaving? Because I turned this conditions into fuel to my discipline, this is why I am is where I am today. I see all the comments, even tho I had 0 notifications because of issues with X. I’m forever grateful.

I want to be really honest. The tweet from Opensea means more than just a text to me. This is a reminder that’s I’m on the right path, and I put my blood and tears to get into position where I’m today. I live in a warzone, I see crazy stuff everyday out of my window for the past 3,5 years. I draw stuff while drones literally exploding on the background. You would ask why I’m not leaving? Because I turned this conditions into fuel to my discipline, this is why I am is where I am today. I see all the comments, even tho I had 0 notifications because of issues with X. I’m forever grateful.

Bunya🏀

14,596 görüntüleme • 1 yıl önce

Building virtual sets on top of reality, moving around in them and seeing them transformed entirely with Gen AI. Is this the future? :D This was quite simple to make actually: Figmin XR running on my Quest 3, using some models from sketchfab and some custom creations to set-dress my hallway. Filming a small performance of walking through the set and then using Runway Gen 3 to transform that. I'm still missing a lot of control with the AI process, like uploading style frames along the duration of the video.. but overall this is pretty incredible! Can't help but think about how insane it would have been to be able to use Dreams for stuff like this! What do you think? #art #ai

Building virtual sets on top of reality, moving around in them and seeing them transformed entirely with Gen AI. Is this the future? :D This was quite simple to make actually: Figmin XR running on my Quest 3, using some models from sketchfab and some custom creations to set-dress my hallway. Filming a small performance of walking through the set and then using Runway Gen 3 to transform that. I'm still missing a lot of control with the AI process, like uploading style frames along the duration of the video.. but overall this is pretty incredible! Can't help but think about how insane it would have been to be able to use Dreams for stuff like this! What do you think? #art #ai

Martin Nebelong

81,989 görüntüleme • 1 yıl önce

Using my Fable credits on procedural animals, this horse cost $40 in tokens. I'm not sure how to feel about that honestly, an artist could do better for $40 I think, at the same time this is all code, and I can't begin to imagine how long it'd take me to code it, even given my experience. I guess being code means it's worth more than the artist version at $40, with the code I can now make all breeds of horses, where the commission I'd be stuck with one 🤔

Using my Fable credits on procedural animals, this horse cost $40 in tokens. I'm not sure how to feel about that honestly, an artist could do better for $40 I think, at the same time this is all code, and I can't begin to imagine how long it'd take me to code it, even given my experience. I guess being code means it's worth more than the artist version at $40, with the code I can now make all breeds of horses, where the commission I'd be stuck with one 🤔

Ryan Fitzpatrick

110,132 görüntüleme • 4 gün önce

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

MrNeRF

51,650 görüntüleme • 2 ay önce

In feature film VFX, our lighting teams spend a lot of time shaving off as much render time as possible. Even 1 minute saved per frame adds up. For a 5 second shot, 1 minute less per frame saves you 2 hours of rendering per shot. Our shots are usually a lot heavier than this and our teams usually get a lot more optimization for render times than 1 minute per frame. Some shots can be rendering for up to 30 hours per frame depending on complexity. Large studios have a plethora of optimization tools which shave off a lot of render time from our shots. Rendering technology is always improving, bringing render times down and increasing physical accuracy. Video is of Hyperion with interactive rendering with Pixar's RenderMan XPU renderer, which is up to 5x faster than their older RIS architecture.

In feature film VFX, our lighting teams spend a lot of time shaving off as much render time as possible. Even 1 minute saved per frame adds up. For a 5 second shot, 1 minute less per frame saves you 2 hours of rendering per shot. Our shots are usually a lot heavier than this and our teams usually get a lot more optimization for render times than 1 minute per frame. Some shots can be rendering for up to 30 hours per frame depending on complexity. Large studios have a plethora of optimization tools which shave off a lot of render time from our shots. Rendering technology is always improving, bringing render times down and increasing physical accuracy. Video is of Hyperion with interactive rendering with Pixar's RenderMan XPU renderer, which is up to 5x faster than their older RIS architecture.

Rassoul Edji

16,286 görüntüleme • 1 yıl önce