📢Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image📢 We directly regress neural parametric head models (NPHMs) from a single image — fast, stable, and significantly more expressive than classical 3DMMs such as FLAME.

📢Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image📢 We directly regress neural parametric head models (NPHMs) from a single image — fast, stable, and significantly more… | Matthia...

📢Pix2NPHM: Learning to Regress NPHM Reconstructions From a Single Image📢 We directly regress neural parametric head models (NPHMs) from a single image — fast, stable, and significantly more expressive...

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📢 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

Today in our TUM AI - Lecture Series we'll have the amazing Ruiqi Gao, Google DeepMind. She'll talk about "𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐰𝐨𝐫𝐥𝐝 𝐦𝐨𝐝𝐞𝐥𝐬: progress and challenges". Live stream: www.youtube.com/live/CkOSMqw... 7pm GMT+1 / 10am PST (Tue Dec 16th).

TUM AI Lecture Series - Building generative world models: progress and challenges (Ruiqi Gaoi)

Abstract: Equipping AI models with the ability to imagine, reason, and act in the physical world is a crucial step toward achieving Artificial General Intelligence (AGI). Generative world models, whic...

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📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text.

#ICCV last week was incredible — catching up with so many people, chatting about research, and, most importantly, having lots of fun. Still hard to fathom this privilege as a researcher — getting to travel to such amazing places and be part of this brilliant community - Thanks!

The hot topic at #ICCV2025 was World Models. They come in different flavors — (interactive) video models, neural simulators, reconstruction models, etc. — but the overarching goal is clear: Generative AI that predict and simulate how the real world works.

Hawaii on the same scale as the United Kingdom.

𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑜 𝑒𝑟𝑔𝑜 𝑠𝑢𝑚 — I generate, therefore I am.

for more documentation: github.com/scannetpp/sc... Huge thanks to Yueh-Cheng Liu, as well as Chandan Yeshwanth and @niessner.bsky.social for their incredible work!

GitHub - scannetpp/scannetpp: [ICCV 2023 Oral] ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes

[ICCV 2023 Oral] ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes - scannetpp/scannetpp

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In the 'early days' of modern deep learning (2012-2015) when ConvNets such as AlexNet or VGG came out, it was considered almost impractical to train an ImageNet classifier from scratch.

Fantastic retreat this weekend by our research groups! Internal reviews, ideas brainstorming, paper reading, and much more! Of course also many social activities -- the highlight being our kayaking trip - lots of fun :)

All six of our submissions were accepted to #NeurIPS2025 🎉🥳 Awesome works about Gaussian Splatting Primitives, Lighting Estimation, Texturing, and much more GenAI :) Great work by Peter Kocsis, Yujin Chen, Zhening Huang, Jiapeng Tang, Nicolas von Lützow, Jonathan Schmidt 🔥🔥🔥

Can we use video diffusion to generate 3D scenes? 𝐖𝐨𝐫𝐥𝐝𝐄𝐱𝐩𝐥𝐨𝐫𝐞𝐫 (#SIGGRAPHAsia25) creates fully-navigable scenes via autoregressive video generation. Text input -> 3DGS scene output & interactive rendering! 🌍http://mschneider456.github.io/world-explorer/ 📽️https://youtu.be/N6NJsNyiv6I

ScaffoldAvatar: High-Fidelity Gaussian Avatars with Patch Expressions (#SIGGRAPH) We reconstruct ultra-high fidelity photorealistic 3D avatars capable of generating realistic and high-quality animations including freckles and other fine facial details. shivangi-aneja.github.io/projects/sca...

📢 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. 📷https://youtu.be/ecK9m3LXg2c 🌍https://litereality.github.io

Seven papers accepted at #ICCV2025! Exciting topics: lots of generative AI using transformers, diffusion, 3DGS, etc. focusing on image synthesis, geometry generation, avatars, and much more - check it out! So proud of everyone involved - let's go🚀🚀🚀 niessnerlab.org/publications...

Want to work on cutting-edge #AI? We have several fully-funded 𝐏𝐡𝐃 & 𝐏𝐨𝐬𝐭𝐃𝐨𝐜 𝐨𝐩𝐞𝐧𝐢𝐧𝐠𝐬 in our Visual Computing & AI Lab in Munich! Apply here: application.vc.in.tum.de Topics have a strong focus on Generative AI, 3DGs, NeRFs, Diffusion, LLMs, etc.

#CVPR submissions per year have significantly increased. Now over 11k / year with an expectation to grow even further. This comes with a lot of implications, how to handle the reviews, presentations, etc. Kudos to the organizers for all the efforts that went into it.

Super excited to be in Nashville for #CVPR2025! Looking forward to catching up with everyone -- feel free to reach out if you want to chat! Everything is a Honky Tonk :)

📢BecomingLit: Relightable Gaussian Avatars with Hybrid Neural Shading📢 We propose a hybrid neural shading scheme for creating intrinsically decomposed 3DGS head avatars, that allow real-time relighting and animation. 🌍https://lnkd.in/evNt8bV2 📷https://lnkd.in/ekB5QeEK

📢Code Release of Pixel3DMM 📢 Looking for a robust and accurate face tracker? We handle challenging in-the-wild settings, such as extreme lighting conditions, fast movements, and occlusions. 👨‍💻https://lnkd.in/e3dX23WV 🌍https://lnkd.in/eQ3Zpn3J Pixel3DMM can be run on videos and single images.

📢PBR-SR: Mesh PBR Texture Super Resolution from 2D Image Priors📢 We propose a new optimization to up-sample textures of 3D assets (albedo, roughness, metallic, and normal maps) by leveraging 2D super-resolution models. 📝http://arxiv.org/abs/2506.02846 📽️https://youtu.be/eaM5S3Mt1RM

One of Europe’s top AI researchers raised a $13M seed to crack the ‘holy grail’ of models

One of Europe’s top AI researchers raised a $13M seed to crack the ‘holy grail’ of models | TechCrunch

One of Europe’s most prominent AI researchers, Matthias Niessner, is now the CEO of SpAItial, a startup working on spatial foundation models.

techcrunch.com

🚀🚀🚀Announcing our $13M funding round to build the next generation of AI: 𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 that can generate entire 3D environments anchored in space & time. 🚀🚀🚀 Interested? Join our world-class team: 🌍 spaitial.ai youtu.be/FiGX82RUz8U

SpAItial AI: Building Spatial Foundation Models

YouTube video by SpAItial AI

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📢GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans📢 We 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.

📢Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction📢 -> highly accurate face reconstruction by training powerful VITs via surface normals & UV-coordinates estimation. These cues from our 2D foundation model constrain the 3DMM parameters, achieving great accuracy.

📢 LinPrim: Linear Primitives for Differentiable Volumetric Rendering 📢 We use octahedra or tetrahedra as explicit as volumetric building blocks for gradient-based novel view synthesis - as an alternative to 3D Gaussians with discrete, bounded geometry. 🌍 nicolasvonluetzow.github.io/LinPrim/

📢 SHeaP: Self-Supervised Head Predictor Learned via 2D Gaussians 📢 Given a single input image, we predict accurate 3D head geometry, pose, and expression. 🌍 nlml.github.io/sheap/ 🎥 youtu.be/vhXsZJWCBMA/ Great work by Liam Schoneveld, Davide Davoli, Jiapeng Tang

📢 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.

📢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.