3d Scene Creation Techniques

🧠✨ Blending AI with VFX (02) – Real-world Applications in Flame + ComfyUI + SynthEyes: Extracting 3D Geometry from Image How many times in VFX do we wish we had access to the actual geometry of a scene element… and there’s no one to ask? no 3D team, no scan, no model, just the plate. Well, that’s changing, check some quick examples I made. We can now combine AI-based img to 3D extraction in ComfyUI, camera tracking with Syntheyes from Boris FX , and compositing in Autodesk Media & Entertainment Flame. The result? A smarter, faster, more creative pipeline that makes a huge difference when we deal with product replacement, lighting, texture projection or particle interactions. Modeling a complex object is time-consuming, but now we can generate a 3D geometry from a single image using AI tools. This not only speeds up tasks like screen replacement, glow projection, and interactive FX... it also makes things possible that might’ve been skipped due to time constraints. 🔧 The process: 1️⃣ The shot is tracked in SynthEyes, a classic that’s now embracing AI beautifully: with its own machine learning matte generator, there's no need to roto for matchmoving. You can focus entirely on the track — as it should be. 2️⃣ Grab a carefully selected still from the shot and generate a 3D model using AI-based geometry estimation. I’m currently using the Hunyuan workflow, and after testing, around 80% of the meshes were either good enough or straight-up amazing. The sheer amount of usable geometry you can generate in a single day is… dazing. 3️⃣ Mesh and tracking data are imported into Flame, where compositing begins, now with actual geometry positioned in 3D space using the same camera from the shot. This gives you the power to relight, project textures, and drive particle systems with total control of the scene. ⏱️ Time saved is real, especially when the subject isn’t a cube — but a custom product, a car, or even a human body. And the best part? Quality improves too. Faster and better? That’s a rare win-win in post. 🛠️ What can we do with this geometry? 🔹 Realistic product relighting or replacement 🔹 Texture and reflection mapping 🔹 Particle interaction 🔹 Scene reconstruction for set extensions 🔹 Projection-based cleanup & paintwork 🔹 Custom glows, shadows & light wraps that stick The pipeline is smoother and faster than you'd expect. The results are production-ready. And the creative potential? Only growing... Hunyuan shows amazing understanding of shape and occlusion but there’s always a next gen coming: Amodal3R (code soon to come) looks very smart at predicting occluded elements... and this just keeps evolving. Just another reminder: AI isn’t a buzzword — it’s a tool. When paired with the right software and real VFX experience, it becomes a true force multiplier. More to come! 😉 🐵🚀🪐 #VFX #AI #Compositing #AutodeskFlame #SynthEyes #ImageTo3D #Relighting #VirtualProduction #3DGeometry #VFXWorkflow #Hunyuan #Nuke #ComfyUI

Chat GPT image ➡️ Interactive 3D World No code, No 3D modeling, lots of AI 👇 Here’s I transformed a regular ChatGPT image into a 3D scene: 🖼️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗶𝗺𝗮𝗴𝗲 𝗮𝘀𝘀𝗲𝘁𝘀 – I had ChatGPT generate an image of a pirate village in a vibrant video game style, then I asked it to create a separate image of each individual element in the scene. For example, I created my pirate village image and instructed ChatGPT to “put the house on a plain white background” or “put the palm tree on a white background”. 🛠️ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝟯𝗗 𝗺𝗼𝗱𝗲𝗹𝘀 – Once every element in the scene was created as an individual image, I uploaded each of my images to Tripo AI’s new “Tripo Studio” where I was able to generate 3D models and textures for each element. I could even clean up the topology on each model if I didn’t like how the meshes turned out. 🏝️ 𝗟𝗮𝘆𝗼𝘂𝘁 𝘁𝗵𝗲 𝘀𝗰𝗲𝗻𝗲 – Next, I imported all of my 3D models into Unreal Engine’s first-person game template. I resized and moved the assets around until my scene resembled my original photo. I also added Unreal’s water plugin to easily add some animated waves to my scene. The first-person template is great because it allows you to “walk” around your scene without and extra code. 🧠 And those three steps are the entire process! It’s a beginner friendly flow that allows for some really cool results. Do you see yourself trying a similar process in the future? #ArtificialIntelligence #AI #ChatGPT

Sometimes, your environment is well-built, but it just doesn’t feel dramatic enough. Here’s a 5-step approach to get that stylized, cinematic look using Unreal Engine: 1️⃣ Use a Low Sun Angle Position your Directional Light close to the horizon. It casts long shadows and adds visual drama instantly—sunrise or sunset is your best friend. 2️⃣ Add Fog for Depth Use Exponential Height Fog with subtle tweaks in density and falloff. It helps separate foreground from background and gives your scene atmosphere. 3️⃣ Use Light Shafts (God Rays) Activate them in your Directional Light to simulate light scattering through trees, windows, or fog—great for magical or emotional scenes. 4️⃣ Color Grade the Scene Use Post Process Volume to adjust temperature, contrast, and saturation. Warm tones for nostalgic scenes, cool for mystery or tension. 5️⃣ Add Cinematic Shadows Use soft shadow settings (like Ray Traced Distance Shadows or Lumen) to avoid harsh edges and give your lighting a filmic softness. Share your favorite techniques! #UnrealEngine #CinematicLighting #EnvironmentDesign #GameArt #RealTimeRendering #LightingTips

Sparse-to-Complete: From Sparse Image Captures to Complete 3D Scenes https://lnkd.in/d2qvuzXH We introduce S2C-3D, a novel sparse-view 3D reconstruction framework for high-fidelity and complete scene reconstruction from as few as six to eight images. Our framework features three components: a specialized diffusion model for scene-specific image restoration, a training-free view-consistency conditioned sampling process in the diffusion model for refined Gaussian optimization, and a camera trajectory planning scheme to ensure comprehensive scene coverage. The specialized diffusion model is developed by finetuning a pretrained architecture on the input views and their corresponding degraded counterparts. The adaptation to the scene distribution allows the model to repair Gaussian renderings while effectively eliminating domain gaps. Meanwhile, the trajectory planning scheme optimizes scene coverage by connecting each newly sampled camera to its two nearest neighbors. By iteratively constructing paths and retaining only those that significantly enhance visibility, the scheme establishes a trajectory that covers the entire scene. To address multi-view conflicts, the view-consistency conditioned sampling process quantifies the consistency between neighboring repaired images. This information is injected as a condition into the sampling process of the frozen diffusion model, facilitating the generation of view-consistent images without additional training. Consequently, our approach produces high-fidelity 3D Gaussians that are robust to artifacts. Experimental results demonstrate that S2C-3D outperforms state-of-the-art methods, constructing high-quality scenes that are free from missing regions, blurring, or other artifacts with very sparse inputs. The source code and data are available at this https URL. --- Newsletter https://lnkd.in/emCkRuA More story https://lnkd.in/eMFcEekQ LinkedIn https://lnkd.in/ehrfPYQ6 #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning #ComputerVision

AI tools are evolving fast, but how do you actually use them in a professional 3D pipeline? Right now, there isn’t a single AI solution that can take you from concept to production-ready content without intervention and that’s why understanding when and how to use AI is more important than ever. For this scene, I started with an AI-generated image of a stone giant, then turned it into a full 3D character by combining different tools: ✅ Tripo for converting 2D to 3D ✅ Mixamo for rigging & animation ✅ Unreal Engine for world-building and final integration Each tool played a specific role. AI helped me speed up the process, but I was still in control of the design, animation, and final composition. The biggest mistake I see in AI-driven content? Relying on a single AI-generated output without refining it. Right now, the best workflows aren’t “one-click AI,” but a mix of traditional 3D techniques and multiple AI tools with each optimized for a specific task. At Radical Realities, we focus on harnessing AI where it makes sense while keeping the final result cinematic, polished, and free from that ‘AI-generated’ look. 📢 How are you using AI in your creative workflows? Have you found certain tools that blend well with traditional techniques? Let’s compare notes. 👇

The original Gaussian Splatting team is back again with a new iteration with under a minute 3DGS reconstruction including poses and point cloud! 🤯🤯 Many clever CUDA tricks, a fast image matching and pose estimation. 💥💥 Inria, Université Côte d'Azur  and Technische Universität Wien present "On-the-fly Reconstruction for Large-Scale Novel View Synthesis from Unposed Images". Radiance field methods such as 3D Gaussian Splatting (3DGS) allow easy reconstruction from photos, enabling free-viewpoint navigation. Nonetheless, pose estimation using Structure from Motion and 3DGS optimization can still each take between minutes and hours of computation after capture is complete. SLAM methods combined with 3DGS are fast but struggle with wide camera baselines and large scenes. They present an on-the-fly method to produce camera poses and a trained 3DGS immediately after capture. Their method can handle dense and wide-baseline captures of ordered photo sequences and large-scale scenes. To do this, they first introduce fast initial pose estimation, exploiting learned features and a GPU-friendly mini bundle adjustment. They then introduce direct sampling of Gaussian primitive positions and shapes, incrementally spawning primitives where required, significantly accelerating training. These two efficient steps allow fast and robust joint optimization of poses and Gaussian primitives. Their incremental approach handles large-scale scenes by introducing scalable radiance field construction, progressively clustering 3DGS primitives, storing them in anchors, and offloading them from the GPU. Clustered primitives are progressively merged, keeping the required scale of 3DGS at any viewpoint. They evaluate our solution on a variety of datasets and show that it can provide on-the-fly processing of all the capture scenarios and scene sizes we target. At the same time our method remains competitive -- in speed, image quality, or both -- with other methods that only handle specific capture styles or scene sizes. More links in comments! #3DGS #gaussiansplatting #neuralradiancefields #densereconstruction #novelviewsynthesis

📢SAM 3D: Single-Image 3D Reconstruction with Foundation-Model Reliability In this week’s deep dive, we break down SAM 3D, Meta’s groundbreaking framework that redefines what’s possible in single-image 3D reconstruction. Unlike earlier pipelines that struggle with occlusions, clutter, and ambiguous textures, SAM 3D produces high-quality 3D shape, texture, and layout directly from a single natural image - and does so with the stability and generalization of a true foundation model. SAM 3D combines a two-stage 3D generative architecture, a massive model-in-the-loop data engine, and a multi-stage synthetic-to-real training curriculum to achieve unprecedented reconstruction fidelity. From indoor scenes to outdoor environments, from tiny objects to full building façades, SAM 3D consistently outperforms traditional 3D methods and even modern diffusion-based models in accuracy, detail, and robustness. Whether you're reconstructing a chair in your living room or digitizing complex real-world scenes, SAM 3D delivers artist-level 3D assets with remarkable consistency - unlocking new possibilities across robotics, AR/VR, gaming, film, simulation, and digital twins. What’s Covered? ✅How SAM 3D Achieves Reliable Single-Image 3D Reconstruction ✅The Geometry Model: Coarse Shape & Layout Prediction ✅The Texture & Refinement Model Explained ✅Synthetic → Semi-Synthetic → Real-World: The Multi-Stage Training Pipeline ✅Model-in-the-Loop Data Engine & Human Preference Alignment (DPO) ✅ How SAM 3D Keeps Getting Better This blog post deconstructs every technical component of SAM 3D - from its architecture and training philosophy to its datasets, refinement modules, and real-world performance. Written to be both technically rigorous and beginner-friendly, the blog post helps researchers, engineers, and creators understand not just how SAM 3D works, but why it works, and what makes it arguably one of the most significant advancements in modern 3D perception. 🔗 Read More: https://lnkd.in/gU8wReJc #SAM3D #MetaAI #ComputerVision #3DReconstruction #FoundationModels #GenerativeAI #3DVision #Robotics #ARVR #GraphicsResearch #AIResearch #SingleImage3D