Norman Müller

Check out MultiDiff #CVPR2024! From a single RGB image, MultiDiff enables scene-level novel view synthesis with free camera control. sirwyver.github.io/MultiDiff youtu.be/SKpFFtVJo9c Great work by @normanisation @K_S_Schwarz @barbara_roessle, L Porzi, S Rota Bulò, P Kontschieder

Congrats to @Normanisation for his successful PhD defense 🥳🎓 Norman's thesis about 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐨𝐧 𝟑𝐃 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 makes important contributions to the 3D vision community. For instance, DiffRF, a generative approach directly operating in 3D space, was among the first diffusion techniques for neural radiance fields. This led to many follow up works in this area and sparked interest across the computer vision community, establishing generative approaches as a corner stone in the 3D domain. Also after his PhD, Norman continues to work on the forefront in computer vision, such as his contributions to MapAnything, a universal feedforward approach for 3D reconstruction. Check out Norman's amazing work: normanm.de Congratulations Dr. Mueller - super proud!

Microsoft’s MAI-Image-2 enters the top three AI image generators in the world thenextweb.com/news/microsoft… via @thenextweb

I am working on building teams for the likes of @NandoDF and @asadovsky for @MicrosoftAI! We are #HiringNow in Europe and US 🧵👇 - follow me for more updates on our Superintelligence Lab - Roles below 🔔

Video gen models excel at replacing humans in front of the camera. But what if they could augment & enhance our performance instead? 🧵 (1/6) 🎬EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers edit-yourself.github.io

Introducing ShapeR, a method for robust conditional 3D shape generation from casually captured sequences. ShapeR leverages a rectified flow transformer conditioned on per-object multimodal data to turn casual image sequences into full metric scene reconstructions. Project Page: facebookresearch.github.io/ShapeR Paper: arxiv.org/abs/2601.11514 Links to code and huggingface below ⬇️

Was gonna wait to announce this 😅 🚨 but yeah new checkpoints just dropped!! Pull latest code and update hf cache - it's a direct slot in & just a better MapA 😉 HF demo also updated: huggingface.co/spaces/faceboo… Stay tuned for more details & 3DV camera ready ⏳

See you later at the #iccv25 Oral Session 6B (Kalākaua Ballroom) at 1PM and poster 356 from 2:30PM! We will present our paper “Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation” with @Normanisation Project + Code: simonelli-andrea.github.io/easy3d

📢 SceneComp @ ICCV 2025 🏝️ 🌎 Generative Scene Completion for Immersive Worlds 🛠️ Reconstruct what you know AND 🪄 Generate what you don’t! 🙌 Meet our speakers @angelaqdai, @holynski_, @jampani_varun, @ZGojcic @taiyasaki, Peter Kontschieder scenecomp.github.io #ICCV2025

📢Excited to be at #ICLR2025 for our paper: VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control Poster: Thu 3-5:30 PM (#134) Website: snap-research.github.io/vd3d/ Code: github.com/snap-research/… Also check out our #CVPR2025 follow-up AC3D: snap-research.github.io/ac3d/

Tired of staring at GS reconstructions? Check out our new method for 3D Interactive Segmentation💥 Easy3D: A Simple Yet Effective Method for 3D Interactive Segmentation Project: simonelli-andrea.github.io/easy3d Paper: arxiv.org/pdf/2504.11024 👇Real-time VR interaction on a GS scene👇