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📢 LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans🏠✨ -> converts RGB-D scans into compact, realistic, and interactive 3D scenes — featuring high-quality meshes, PBR materials, and articulated objects. 📷 🌍

Matthias Niessner

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23,805 Aufrufe • vor 1 Jahr •via X (Twitter)

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📢GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans📢 We present a novel method to reconstruct hair strands from colorless 3D scans by extracting orientation cues directly from the mesh surface geometry by finding local characteristic lines and from shaded renderings using a neural 2D line detector. We enhance the reconstruction with a diffusion prior trained on synthetic hair data and adapted to each scan using a tailored text prompt, allowing us to recover both simple and complex hairstyles without relying on color input. To support further research, we also introduce Strands400, the largest publicly available dataset of 3D hair strand reconstructions from real-world scans of 400 different people, featuring complicated hairstyles, such as ponytails and buns. 🌍 📷 Great work by Rachmadio Noval L. Artem Sevastopolsky Egor Zakharov @ness_pris

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Introducing 📦𝗔𝗿𝘁𝗶𝗟𝗮𝘁𝗲𝗻𝘁🔧 (SIGGRAPH Asia 2025) — a high-quality 3D diffusion model that explicitly models object articulation, paving the way for richer, more realistic assets in embodied AI and simulation: – Generates fully articulated 3D objects – Physically plausible joints & motion – High-fidelity 3D Gaussian appearance – Supports generation from a single real image arXiv: Project: Code (coming soon):

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DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery Abstract: Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.

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GSTAR: Gaussian Surface Tracking and Reconstruction Contributions: • A new framework for tracking and reconstructing dynamic scenes, combining 3D Gaussians and meshes to effectively manage changes in topology. • A method for Gaussian unbinding and surface re-meshing, allowing for the generation of new surfaces as topologies evolve. • A method for handling large or fast deformations of surfaces between frames using scene flow warping. Abstract (excerpt): However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appearing, disappearing, or splitting. To address these challenges, we propose GSTAR, a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. Given multi-view captures as input, GSTAR binds Gaussians to mesh faces to represent dynamic objects. For surfaces with consistent topology, GSTAR maintains the mesh topology and tracks the meshes using Gaussians.

GSTAR: Gaussian Surface Tracking and Reconstruction Contributions: • A new framework for tracking and reconstructing dynamic scenes, combining 3D Gaussians and meshes to effectively manage changes in topology. • A method for Gaussian unbinding and surface re-meshing, allowing for the generation of new surfaces as topologies evolve. • A method for handling large or fast deformations of surfaces between frames using scene flow warping. Abstract (excerpt): However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appearing, disappearing, or splitting. To address these challenges, we propose GSTAR, a novel method that achieves photo-realistic rendering, accurate surface reconstruction, and reliable 3D tracking for general dynamic scenes with changing topology. Given multi-view captures as input, GSTAR binds Gaussians to mesh faces to represent dynamic objects. For surfaces with consistent topology, GSTAR maintains the mesh topology and tracks the meshes using Gaussians.

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With Hunyuan3D World Model 1.0 now released and open-sourced, we're excited to showcase the technical highlights behind this impressive innovation: ✅360° Panoramic Generation: Creates complete, immersive “world scenes”, far beyond localized views. ✅Explorable 3D Scene Generation: Generates diverse, spatially consistent 3D worlds from text/image for truly immersive exploration. ✅Interactive/Editable: Achieves separation of foreground objects, background terrain, ground, and sky, for seamless secondary editing. ✅Exportable Mesh: Generated scenes can be exported as 3D meshes for direct import into mainstream game engines and modeling software. ✅Industry-Leading SOTA Evaluation: Surpasses state-of-the-art open-source models in generation quality. As the industry's first open-source model for physical simulation and explorable world generation, Hunyuan3D World Model 1.0 aims to foster a collaborative community ecosystem with developers and enthusiasts. ✨ Try it now: 🤗 Hugging Face:

With Hunyuan3D World Model 1.0 now released and open-sourced, we're excited to showcase the technical highlights behind this impressive innovation: ✅360° Panoramic Generation: Creates complete, immersive “world scenes”, far beyond localized views. ✅Explorable 3D Scene Generation: Generates diverse, spatially consistent 3D worlds from text/image for truly immersive exploration. ✅Interactive/Editable: Achieves separation of foreground objects, background terrain, ground, and sky, for seamless secondary editing. ✅Exportable Mesh: Generated scenes can be exported as 3D meshes for direct import into mainstream game engines and modeling software. ✅Industry-Leading SOTA Evaluation: Surpasses state-of-the-art open-source models in generation quality. As the industry's first open-source model for physical simulation and explorable world generation, Hunyuan3D World Model 1.0 aims to foster a collaborative community ecosystem with developers and enthusiasts. ✨ Try it now: 🤗 Hugging Face:

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PARTNERSHIP: AI is anticipated to reach $1.8 trillion by 2030. The GameFi sector is expected to grow to $50 billion by 2028. The tourism industry is projected to grow to $14 trillion by 2028 and the 3D mapping market is expected to reach $12 billion by 2030. What happens when you bridge these three mega-industries? You get NeoTech, the world's first micro low latency global RWA 3D mapping system. Simply put, Neotech’s technology combines precise 360 high-quality scans and generates point clouds and usable 3D objects of entire cities, counties, and countries. These assets are then tokenized into tradable NFTs, creating opportunities for monetization. The project’s metrics already look stellar: * 500 cities scanned * 25,000+ km of covered area * 7 countries processed * 1,000+ scans ready for deployment $NEOT is coming. Know more on Disclaimer: This is a paid partnership, DYOR as this is not financial advice.

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Our customizable 3D vtuber model now has hair and transparency!! :D We haven't seen any Live2D styled customizable models made for 3D yet, so we want to make a high quality model with lots of tech and features! -^u^- If you're interested, please let us know with VGen's 'get notified' feature!✨It would make all the effort Palette and I are putting into this all the more worthwhile!

Pastell & Palette🤍 3D Customizable Model!

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

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

MrNeRF

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

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

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34,303 Aufrufe • vor 8 Monaten

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

AK

633,674 Aufrufe • vor 3 Jahren

World Model is trending— let's revisit our HunyuanWorld journey. We’ve been pioneering open-source 3D world generation in the past two months, and this ride’s only getting started. 🌍 📅 July: HunyuanWorld 1.0 📌 First open-source 3D world model compatible with CG pipelines (Unity/Unreal/Blender) 📌 Hit 2K+ GitHub stars in just two months ⭐—thank you for the love! 📅 August: 1.0-Lite 📌Same top-tier quality, running on consumer GPUs! 📅 September: 1.0-Voyager 📌 Direct 3D output + world memory—taking exploration further! Seamlessly integrated into CG pipelines with layered 3D modeling (assets, terrain, skybox) and fully open-sourced.. we’re fully committed to building open-source spatial intelligence for all! 🚀 💡 Why it matters? ✅ Seamless CG Pipeline Integration: Export generated 3D scenes as standard mesh formats, effortlessly integrating into industry-standard tools like Blender, Unity, and Unreal Engine for direct editing, animation, and physical simulation. ✅ Hierarchical Scene Editing: Deconstruct scenes into semantic layers (sky, background, foreground objects) via instance recognition and layer decomposition, allowing for atomic-level control—independently modify, relocate, or replace objects without rebuilding the entire world. Project page: Github: Amazing creations by Stijn Spanhove camenduru GENEL | AIを用いた動画制作 apolinario 🌐 とりにく Directive Creator 🪥 👇 #AI #3DGeneration #OpenSource #WorldModels #Hunyuan3D #HunyuanWorld

World Model is trending— let's revisit our HunyuanWorld journey. We’ve been pioneering open-source 3D world generation in the past two months, and this ride’s only getting started. 🌍 📅 July: HunyuanWorld 1.0 📌 First open-source 3D world model compatible with CG pipelines (Unity/Unreal/Blender) 📌 Hit 2K+ GitHub stars in just two months ⭐—thank you for the love! 📅 August: 1.0-Lite 📌Same top-tier quality, running on consumer GPUs! 📅 September: 1.0-Voyager 📌 Direct 3D output + world memory—taking exploration further! Seamlessly integrated into CG pipelines with layered 3D modeling (assets, terrain, skybox) and fully open-sourced.. we’re fully committed to building open-source spatial intelligence for all! 🚀 💡 Why it matters? ✅ Seamless CG Pipeline Integration: Export generated 3D scenes as standard mesh formats, effortlessly integrating into industry-standard tools like Blender, Unity, and Unreal Engine for direct editing, animation, and physical simulation. ✅ Hierarchical Scene Editing: Deconstruct scenes into semantic layers (sky, background, foreground objects) via instance recognition and layer decomposition, allowing for atomic-level control—independently modify, relocate, or replace objects without rebuilding the entire world. Project page: Github: Amazing creations by Stijn Spanhove camenduru GENEL | AIを用いた動画制作 apolinario 🌐 とりにく Directive Creator 🪥 👇 #AI #3DGeneration #OpenSource #WorldModels #Hunyuan3D #HunyuanWorld

Tencent HY

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ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies Contributions: 1) We propose ImmerseGen, a novel agent-guided 3D environment generation framework. It uses simplified geometric proxies with alpha-textured meshes to produce compact, photorealistic worlds ready for real-time mobile VR rendering. 2) We propose a novel RGBA texturing paradigm. It first synthesizes 8K terrain textures using a geometry-conditioned panorama generator via user-centric mapping, and then directly generates alpha-textured proxy assets, avoiding fidelity loss typically resulting from mesh decimation. 3) To automate scene creation from user prompts, we introduce VLM-based modeling agents equipped with a novel grid-based semantic analysis. This enables 3D spatial reasoning from 2D observations and ensures accurate asset placement. ImmerseGen further enhances immersion with dynamic effects and ambient audio for a multisensory experience. 4) Experiments on multiple scene-generation scenarios and live mobile VR applications show that ImmerseGen outperforms previous methods in visual quality, realism, spatial coherence, and rendering efficiency for immersive real-time VR experiences.

MrNeRF

14,225 Aufrufe • vor 1 Jahr

$📢Announcing our 3D head avatar benchmark📢 Two tasks with hidden test sets: - Dynamic Novel View Synthesis on Heads - Monocular FLAME-driven Head Avatar Reconstruction Our goal is to make research on 3D head avatars more comparable and ultimately increase the realism of digital humans. The benchmark studies distinct phenomena of 3D head avatar creation, such as extreme facial expressions, slow motion captures of shaking long hair, or complicated light reflection and refraction patterns of glasses. The two benchmark tasks assess two core desiderata of 3D avatars: While the novel view synthesis challenge focuses on best possible rendering quality of complex moving scenes, the avatar animation challenge is concerned with how well a driving signal is translated into an avatar. Evaluations are light-weight and consist of diverse video recordings from the popular NeRSemble dataset with a hidden test set. Participation in the benchmark is therefore straight-forward and requires only 5 reconstructions per task. Leaderboard and benchmark submission: Benchmark data access and toolkit: Great work by Tobias Kirschstein Simon Giebenhain$

📢Announcing our 3D head avatar benchmark📢 Two tasks with hidden test sets: - Dynamic Novel View Synthesis on Heads - Monocular FLAME-driven Head Avatar Reconstruction Our goal is to make research on 3D head avatars more comparable and ultimately increase the realism of digital humans. The benchmark studies distinct phenomena of 3D head avatar creation, such as extreme facial expressions, slow motion captures of shaking long hair, or complicated light reflection and refraction patterns of glasses. The two benchmark tasks assess two core desiderata of 3D avatars: While the novel view synthesis challenge focuses on best possible rendering quality of complex moving scenes, the avatar animation challenge is concerned with how well a driving signal is translated into an avatar. Evaluations are light-weight and consist of diverse video recordings from the popular NeRSemble dataset with a hidden test set. Participation in the benchmark is therefore straight-forward and requires only 5 reconstructions per task. Leaderboard and benchmark submission: Benchmark data access and toolkit: Great work by Tobias Kirschstein Simon Giebenhain

Matthias Niessner

28,094 Aufrufe • vor 1 Jahr

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share PhysTwin: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines Gaussian splatting with inverse dynamics optimization based on simple spring-mass systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

Yunzhu Li

25,279 Aufrufe • vor 1 Jahr

img2threejs is back. I’m getting ready to ship v1.3. Highlights: - Divine Eye: deterministic render validation before the final VLM review. - Better geometry reconstruction for more accurate 3D shapes. - Photo-grounded PBR materials instead of generic guesses. - Stronger reference validation. - Faster generation with smarter caching. These improvements were originally planned for v2.0, but the scope kept growing, so I decided to split the release instead of making everyone wait. Here's a demo from v1.3. I’d love your feedback: - Does the output quality look good? - Is there anything missing before I release v1.3? - Any issues you notice? If everything looks solid, I’ll publish v1.3 today ( in next 6hour ) v2.0 is still coming. I’m taking extra time to test and polish it to make sure it delivers the best quality possible.

img2threejs is back. I’m getting ready to ship v1.3. Highlights: - Divine Eye: deterministic render validation before the final VLM review. - Better geometry reconstruction for more accurate 3D shapes. - Photo-grounded PBR materials instead of generic guesses. - Stronger reference validation. - Faster generation with smarter caching. These improvements were originally planned for v2.0, but the scope kept growing, so I decided to split the release instead of making everyone wait. Here's a demo from v1.3. I’d love your feedback: - Does the output quality look good? - Is there anything missing before I release v1.3? - Any issues you notice? If everything looks solid, I’ll publish v1.3 today ( in next 6hour ) v2.0 is still coming. I’m taking extra time to test and polish it to make sure it delivers the best quality possible.

Nick

52,003 Aufrufe • vor 22 Tagen

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,202 Aufrufe • vor 9 Monaten

When a peaceful walk turns into an epic fantasy boss fight. Created with Seedance 2.0 Prompt: cinematic, high-action scene set in a misty, rural rice paddy field in Asia. A brave male warrior dressed in traditional fantasy armor stands on a narrow mud path, facing a colossal, terrifying red dragon emerging from the dense fog and water. The warrior is wielding magical green energy, casting glowing spells from his hands. Intense battle sequence with dramatic camera angles, dynamic movement, splashing water, swirling fog, and particle effects. In a climactic moment, the warrior unleashes a powerful green energy beam that hits the dragon, causing it to explode into a massive cloud of red petals and debris. High-quality 3D animation style, epic atmosphere, hyper-realistic textures. #AIAnimation #FantasyArt

When a peaceful walk turns into an epic fantasy boss fight. Created with Seedance 2.0 Prompt: cinematic, high-action scene set in a misty, rural rice paddy field in Asia. A brave male warrior dressed in traditional fantasy armor stands on a narrow mud path, facing a colossal, terrifying red dragon emerging from the dense fog and water. The warrior is wielding magical green energy, casting glowing spells from his hands. Intense battle sequence with dramatic camera angles, dynamic movement, splashing water, swirling fog, and particle effects. In a climactic moment, the warrior unleashes a powerful green energy beam that hits the dragon, causing it to explode into a massive cloud of red petals and debris. High-quality 3D animation style, epic atmosphere, hyper-realistic textures. #AIAnimation #FantasyArt

Avelyrah

12,601 Aufrufe • vor 26 Tagen

Tiny Gugugaga penguins are ready to party Seedance 2.0 on Yapper Prompt smooth fluid AI animation of multiple cute chibi Gugugaga penguin characters from Arknights Endfield displayed as 3D figurines on a shiny white plate, the little penguins subtly blink, tilt heads cutely, wiggle bodies and make tiny happy movements while staying in formation, gentle slow camera pan across the group with slight zoom, kawaii chibi anime style blended with realistic figurine lighting and reflections, adorable wholesome meme vibe, high detail textures, soft pastel glows and sparkles, original gugugaga baby-talk cooing sound, Arknights Endfield aesthetic --motion medium --ar 9:16 --quality high #WutheringWaves #流萤 #Firefly #ホタル #HonkaiStarRail📷 #崩壊スターレイル #AIイラスト #AIart️️️️️️️️️️️️️️️️️️️️️ #ArknightsEndfield #gugugaga #seedance2

Tiny Gugugaga penguins are ready to party Seedance 2.0 on Yapper Prompt smooth fluid AI animation of multiple cute chibi Gugugaga penguin characters from Arknights Endfield displayed as 3D figurines on a shiny white plate, the little penguins subtly blink, tilt heads cutely, wiggle bodies and make tiny happy movements while staying in formation, gentle slow camera pan across the group with slight zoom, kawaii chibi anime style blended with realistic figurine lighting and reflections, adorable wholesome meme vibe, high detail textures, soft pastel glows and sparkles, original gugugaga baby-talk cooing sound, Arknights Endfield aesthetic --motion medium --ar 9:16 --quality high #WutheringWaves #流萤 #Firefly #ホタル #HonkaiStarRail📷 #崩壊スターレイル #AIイラスト #AIart️️️️️️️️️️️️️️️️️️️️️ #ArknightsEndfield #gugugaga #seedance2

Sharon Riley

42,293 Aufrufe • vor 4 Monaten

$GM my friends!🌞 Are you ready to fight?!😏🔥 Megan Fox, Kat Dennings 💪🏻 👉🏻Subscribe for more content!😎 Nano Banana 2 viao Hailuo AI & Kling 3.0 via Higgsfield AI Prompt: Hyper-realistic photorealistic portrait of cosplay Megam fox as Chun-Li from Street Fighter, crouching pose with hands on the ground, looking slightly directly to the camera with a neutral expression, extremely detailed face, 8k resolution, photorealistic skin texture with visible pores, subtle skin imperfections, realistic sweat droplets, micro skin details, individual eyelashes, realistic brown eyes with detailed iris texture and catchlights, natural subsurface scattering, ultra-detailed black hair with individual strands and realistic highlights, white silk ribbons in hair.She wears the classic blue qipao dress with gold embroidery, white high heels stilettos, black spiked leather wristbands with metallic studs, highly detailed fabric textures, realistic cloth folds and wrinkles, reflective surfaces on the dress. Torn paper collage effect in the background with two close-up realistic eye photos (one left, one right) overlapping the main image.Shot on Sony A1 camera with Sony FE 85mm f/1.4 GM lens, f/2.0 aperture, 1/160s shutter speed, ISO 100, cinematic natural studio lighting, soft key light from top left, subtle rim lighting, dramatic yet clean white background, sharp focus on face and body, shallow depth of field, bokeh effect, ultra-detailed 32k, masterpiece, best quality, photorealism, national geographic level detail, 8k raw photo, hyperrealistic textures, microscopic skin detail, octane render, unreal engine 5 quality. "style_and_quality": { "style": "photorealistic, high-fashion glamour photography, cinematic", "detail_level": "ultra high detail on skin pores, fabric texture, hair strands, reflections", "rendering": "realistic fabric details, glossy materials, accurate light interaction", "avoid": "cartoon, anime, illustration, painting, 3D render look" } }$

GM my friends!🌞 Are you ready to fight?!😏🔥 Megan Fox, Kat Dennings 💪🏻 👉🏻Subscribe for more content!😎 Nano Banana 2 viao Hailuo AI & Kling 3.0 via Higgsfield AI Prompt: Hyper-realistic photorealistic portrait of cosplay Megam fox as Chun-Li from Street Fighter, crouching pose with hands on the ground, looking slightly directly to the camera with a neutral expression, extremely detailed face, 8k resolution, photorealistic skin texture with visible pores, subtle skin imperfections, realistic sweat droplets, micro skin details, individual eyelashes, realistic brown eyes with detailed iris texture and catchlights, natural subsurface scattering, ultra-detailed black hair with individual strands and realistic highlights, white silk ribbons in hair.She wears the classic blue qipao dress with gold embroidery, white high heels stilettos, black spiked leather wristbands with metallic studs, highly detailed fabric textures, realistic cloth folds and wrinkles, reflective surfaces on the dress. Torn paper collage effect in the background with two close-up realistic eye photos (one left, one right) overlapping the main image.Shot on Sony A1 camera with Sony FE 85mm f/1.4 GM lens, f/2.0 aperture, 1/160s shutter speed, ISO 100, cinematic natural studio lighting, soft key light from top left, subtle rim lighting, dramatic yet clean white background, sharp focus on face and body, shallow depth of field, bokeh effect, ultra-detailed 32k, masterpiece, best quality, photorealism, national geographic level detail, 8k raw photo, hyperrealistic textures, microscopic skin detail, octane render, unreal engine 5 quality. "style_and_quality": { "style": "photorealistic, high-fashion glamour photography, cinematic", "detail_level": "ultra high detail on skin pores, fabric texture, hair strands, reflections", "rendering": "realistic fabric details, glossy materials, accurate light interaction", "avoid": "cartoon, anime, illustration, painting, 3D render look" } }

KeorUnreal

31,169 Aufrufe • vor 2 Monaten