Congrats to @Peter4AI for his successful PhD defense! Peter’s thesis is on 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐢𝐨𝐫𝐬 𝐟𝐨𝐫 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐋𝐢𝐠𝐡𝐭𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐫𝐨𝐥. His arguably most impactful work, Intrinsic Image Diffusion, was among the first to bring diffusion models to material estimation. The key idea is that intrinsic decomposition is inherently ambiguous, so rather than regressing a single answer, it is formulated probabilistically and samples plausible decompositions, leveraging the strong image priors of diffusion models. This opened up a new line of diffusion-based inverse rendering work, including IntrinsicAnything, Marigold-IID, DiffusionRenderer, and many more. Today, generative priors are the standard approach for material estimation and relightable reconstruction. However, Peter has done much more cutting-edge research: LightIt brought explicit lighting control to diffusion models, IntrinsiX enabled direct PBR generation from text, and Intrinsic Image Fusion lifts these priors to high-quality multi-view 3D material reconstruction. In summary, his PhD has shaped how we think about materials and lighting in the age of generative AI. I’m also particularly proud that Peter is continuing this line of research as a Research Scientist at NVIDIA, e.g., his new work on Texture Space Material Diffusion and NeuMatEx. Also thanks to Ruediger Westermann and George Drettakis for being on the committee. Check out Peter's fantastic work: peter-kocsis.github.io Congrats Dr. Kocsis, it was an honor to have you in the lab!

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍peter-kocsis.github.io/Intri… 🎥piped.video/-Vs3tR1Xl7k Great work by @Peter4AI @LukasHollein!

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍peter-kocsis.github.io/Intri… 🎥piped.video/-Vs3tR1Xl7k Great work by @Peter4AI @LukasHollein!

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out: spaitial.ai/

📢 IntrinsiX: High-Quality PBR Generation using Image Priors 📢 From text input, we generate renderable PBR maps! Next to editable image generation, our predictions can be distilled into room-scale scenes using SDS for large-scale PBR texture generation. We first train separate LoRA modules for the intrinsic properties of albedo, rough/metal, normal. Then, we introduce cross-intrinsic attention using a rerendering loss with importance-weighted light sampling to enable coherent PBR generation. Our method outperforms text -> image -> PBR methods both in generalization and quality, since directly generating PBR maps does not suffer from the inherent ambiguity of intrinsic image decomposition. In addition, our design choice facilitates SDS-based PBR texture distillation. 🌍 peter-kocsis.github.io/Intri… 🎥 piped.video/b0wVA44R93Y Great work by @Peter4AI, @LukasHollein

Congrats to @yawarnihal for winning the @MdsiTum best paper award for his amazing 𝐌𝐞𝐬𝐡𝐆𝐏𝐓 work🎉 MeshGPT autoregressively generates compact, artist-style triangle meshes by tokenizing faces into a learned discrete vocabulary (VQ-style codebook) and training a decoder-only transformer to predict those face tokens — because discrete tokenization + attention lets GPT-style models learn long-range geometric & topological patterns and produce coherent, high-fidelity 3D assets. MeshGPT's use cases go far beyond traditional content creation applications in computer graphics. For instance, the method was developed in collaboration with @Audi to help rapid prototyping of car designs, where explicit and precise mesh design are essential. In the research community, there have already been many follow ups such as MeshAnything, MeshXL, Meshtron, and many more - finally, we can use AI to generate high-fidelity 3D content :) Project: nihalsid.github.io/mesh-gpt/ Video: piped.video/UV90O1_69_o