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Introducing Teleport 2.0, a next-generation release for our 3D capture solution that sets a new benchmark in cloud-based Gaussian Splatting. Teleport 2.0 is all about lowering the barrier of entry to create and share lifelike 3D captures. New capabilities include: ▪️ Upgraded 3D reconstruction engine: Preserves real-world detail, even...

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Varjo

14,493 subscribers

21,211 Aufrufe • vor 1 Jahr •via X (Twitter)

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Karl Mehtavor 1 Jahr

A huge leap forward for 3D capture and immersive experiences.

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PowerBeatsVRvor 3 Jahren

VR fitness app PowerBeatsVR is NOW LIVE on the official Meta Quest store! Get fit in VR without any expensive subscription:

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Michelle Roy 🇨🇦vor 1 Jahr

Oh nice!

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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
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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

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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!

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Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

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The AR GenAI solution available on AR Code SaaS continues to demonstrate how fast immersive AR content can now be created from a single image. In this new demo, a contemporary art statuette is captured with a single photo, then instantly transformed into a fully optimized 3D model ready for augmented reality display. As shown in the video, the generated 3D artwork is automatically published through an AR QR Code, allowing anyone to launch the experience instantly in augmented reality directly from their smartphone. No app required. This example highlights a powerful capability of AR GenAI by AR Code: artworks can now be easily digitized and transformed into interactive AR experiences starting from just one photo. For contemporary art galleries, museums, cultural institutions, artists, and art marketplaces, this opens a new path to quickly create scalable 3D collections and immersive exhibitions. Object Photo → AI 3D generation → AR QR Code → Instant WebAR What previously required complex 3D scanning setups, photogrammetry pipelines, or specialized expertise can now be achieved in minutes. AR GenAI makes it possible to digitize artworks, publish AR experiences, and scale immersive cultural content faster than ever. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #ImageTo3D #ImageToAR #WebAR #AugmentedReality #DigitalArt #Museums #ArtTech

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PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

AK

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"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.

Gabriele Romagnoli

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GDN: The $KNDX Solution No more hardware bottlenecks Kondux GDN offers streamlined solutions to the problems users may face these days, such as limited access to state-of-the-art hardware & GPU power. We solve this by offloading heavy computational tasks to GDN while streaming 3D assets in real time to any device, regardless of local device capabilities. This means users can easily perform complex activities like high performance gaming, 3D VFX rendering & digital twin simulations for manufacturing without reliance on local GPU power & hardware. For creators, developers & enterprises, this eliminates massive roadblocks for their creative work flow. Instant collaboration through blockchain security In addition, our smart contracts enables sharing digital assets instant and secure, allowing for easy collaboration across multiple platforms. By utilizing our DNA technology, viewing a 3D asset doesn’t automatically grant ownership, and ensures creators 3D assets are protected from unauthorized use. Our kNFT tech brings life, personality & evolution to digital assets. Part of our mission is facilitating the shift of conversation from only traditional static NFTs, towards dynamic high-fidelity 3D assets with real world utility. Advanced interactivity & immersion in real time for all.

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Om Patel

157,274 Aufrufe • vor 2 Monaten

Dormant Memories — a gallery regrouping my recent experiments built from real-world captures, reconstructed as 3D Gaussian Splats, and in many cases manipulated using World Labs' Marble. Everything is built with Spark, the 3D Gaussian splat renderer released by World Labs. It runs in the browser and can be experienced across devices: desktop, mobile, and headsets like Apple Vision Pro and Quest 3. This work comes out of something I've been exploring for a few years now: capturing fragments of the world (places, objects, moments...) and turning them into spaces that can then be revisited, manipulated, and explored in real time. What particularly interests me here is the process of creating alternate versions of the same place; starting from something that physically exists and using it as a base to explore variations, instead of generating from scratch. These explorations are part of what we do at Dpt.... same tools, same curiosity, same collaborative energy that shape the studio's work on interactive and immersive experiences. Some of these experiments were featured as part of the Spark 2.0 release by World Labs. Their technical blog post is a must-read; link in the thread ↓ These are experiments. They aren't fully optimized, they aren't bug-free, and the UX has rough edges. The goal is to explore, test, and learn. #GaussianSplatting #3DGS #pointcloud #WorldModels #sparkjs #threejs
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Dormant Memories — a gallery regrouping my recent experiments built from real-world captures, reconstructed as 3D Gaussian Splats, and in many cases manipulated using World Labs' Marble. Everything is built with Spark, the 3D Gaussian splat renderer released by World Labs. It runs in the browser and can be experienced across devices: desktop, mobile, and headsets like Apple Vision Pro and Quest 3. This work comes out of something I've been exploring for a few years now: capturing fragments of the world (places, objects, moments...) and turning them into spaces that can then be revisited, manipulated, and explored in real time. What particularly interests me here is the process of creating alternate versions of the same place; starting from something that physically exists and using it as a base to explore variations, instead of generating from scratch. These explorations are part of what we do at Dpt.... same tools, same curiosity, same collaborative energy that shape the studio's work on interactive and immersive experiences. Some of these experiments were featured as part of the Spark 2.0 release by World Labs. Their technical blog post is a must-read; link in the thread ↓ These are experiments. They aren't fully optimized, they aren't bug-free, and the UX has rough edges. The goal is to explore, test, and learn. #GaussianSplatting #3DGS #pointcloud #WorldModels #sparkjs #threejs

Hugues Bruyère

12,408 Aufrufe • vor 3 Monaten

Enter the SPLATRIX! We digitized Henry Pearce and converted him over to 4DGS to be played back in real-time. 3D Gaussians Splats in Motion. 4DGS. Our goal was to improve the quality of our AeonX & 4DGS pipeline, with automated background removal to retain fine hair details as well as to improve overall surface consistency on a full torso capture. We're currently using a post process to temporally filter the 2D frames. We still need to solve 4DGS temporal stability. We're hoping to test with Jonathon Luiten dynamic gaussian code soon... 🙏 for that script! We captured this performance under global illumination. This is an easy task compared to RGB+W lighting, which is going to be much harder! This is our next challenge. We're posting these snippet videos to show our progress with 3D/4DGS. We plan to write a full blog post on our 3D/4DGS journey soon. 3D Gaussian Splatting #GaussianSplatting #inria #radiancefields #ir #aeonx #rgbw #idatronic #ximea #sibr #hdri #60fps More info here - Higher Quality Videos:
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Enter the SPLATRIX! We digitized Henry Pearce and converted him over to 4DGS to be played back in real-time. 3D Gaussians Splats in Motion. 4DGS. Our goal was to improve the quality of our AeonX & 4DGS pipeline, with automated background removal to retain fine hair details as well as to improve overall surface consistency on a full torso capture. We're currently using a post process to temporally filter the 2D frames. We still need to solve 4DGS temporal stability. We're hoping to test with Jonathon Luiten dynamic gaussian code soon... 🙏 for that script! We captured this performance under global illumination. This is an easy task compared to RGB+W lighting, which is going to be much harder! This is our next challenge. We're posting these snippet videos to show our progress with 3D/4DGS. We plan to write a full blog post on our 3D/4DGS journey soon. 3D Gaussian Splatting #GaussianSplatting #inria #radiancefields #ir #aeonx #rgbw #idatronic #ximea #sibr #hdri #60fps More info here - Higher Quality Videos:

Infinite-Realities

90,662 Aufrufe • vor 2 Jahren

🍺 LagerNVS (CVPR 2026) 🍺 LagerNVS is a generalizable, feed-forward, real-time Novel View Synthesis network which - performs rendering in real time, - generalizes to in-the-wild data, - works with and without known source cameras, - sets a new state-of-the-art among deterministic methods, - can be paired with a diffusion decoder for generative extrapolation. LagerNVS shows that 3D biases are useful for Novel View Synthesis but explicit 3D representations are not required to achieve them. We use 3D biases in (1) architecture design and (2) pre-training: (1) In NVS with explicit 3D representations (3DGS, NeRF) reconstruction is typically difficult and slow, but rendering is much faster and simpler. We mimic this process in the network design: we use a large (1B params) encoder and a small, lightweight decoder (ViT-B). This allows increasing the network capacity while still achieving real-time rendering. (2) The encoder, initialized from VGGT, was pre-trained with 3D reconstruction objectives, making the initial features 3D aware. Both substantially improve performance. Project page: Code: Paper: Models: Work done with Jianyuan Minghao Chen Christian Rupprecht and Andrea Vedaldi
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🍺 LagerNVS (CVPR 2026) 🍺 LagerNVS is a generalizable, feed-forward, real-time Novel View Synthesis network which - performs rendering in real time, - generalizes to in-the-wild data, - works with and without known source cameras, - sets a new state-of-the-art among deterministic methods, - can be paired with a diffusion decoder for generative extrapolation. LagerNVS shows that 3D biases are useful for Novel View Synthesis but explicit 3D representations are not required to achieve them. We use 3D biases in (1) architecture design and (2) pre-training: (1) In NVS with explicit 3D representations (3DGS, NeRF) reconstruction is typically difficult and slow, but rendering is much faster and simpler. We mimic this process in the network design: we use a large (1B params) encoder and a small, lightweight decoder (ViT-B). This allows increasing the network capacity while still achieving real-time rendering. (2) The encoder, initialized from VGGT, was pre-trained with 3D reconstruction objectives, making the initial features 3D aware. Both substantially improve performance. Project page: Code: Paper: Models: Work done with Jianyuan Minghao Chen Christian Rupprecht and Andrea Vedaldi

Stan Szymanowicz

31,651 Aufrufe • vor 4 Monaten