Dominik Narnhofer Profile
Dominik Narnhofer

@DNarnhofer

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Zurich, Switzerland
Joined May 2013
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@DNarnhofer
Dominik Narnhofer
1 month
Want to leverage the power of SOTA 3D models like VGGT & Video LDMs for 3D generation? Now you can! πŸš€ Introducing VIST3A β€” we stitch pretrained video generators to 3D foundation models and align them via reward finetuning. πŸ“„ https://t.co/MctMyuDev4 🌐
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gohyojun15.github.io
VIST3A unifies a video generator and a multi-view 3D reconstruction model into a single latent diffusion model that generates 3D representations directly from text.
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@JuliusErbach
Julius Erbach
6 months
πŸš€ Just released: FLAIR – a new training-free approach to solving inverse problems using flow-matching models! 🎯 Try it live: https://t.co/yKYafCQa76 πŸ“š Learn more: https://t.co/7jwB0gfUxm
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@NandoMetzger
Nando Metzger
6 months
@JuliusErbach It works surprisingly well for memes with text:
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@DNarnhofer
Dominik Narnhofer
6 months
πŸš€ Excited to Share: Solving Inverse Problems with FLAIR!Β πŸš€ We present FLAIR, a novel framework for inverse problems using generative flow-based models. Project Page πŸš€ : https://t.co/KZWLqmYy5x arXiv πŸ“œ : https://t.co/5CFS28JDdd Demo πŸ€— :
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huggingface.co
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@AntonObukhov1
Anton Obukhov
9 months
RollingDepth rolls into Nashville for #CVPR2025! 🎸
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@NandoMetzger
Nando Metzger
9 months
We present TheraπŸ”₯: The new SOTA arbitrary-scale super-resolution method with built-in anti-aliasing. Our approach introduces Neural Heat Fields, which guarantee exact Gaussian filtering at any scale, enabling continuous image reconstruction without extra computational cost.
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@gklambauer
GΓΌnter Klambauer
10 months
A Variational Perspective on Generative Protein Fitness Optimization Uses an approach to optimize protein fitness in latent space. Famous AAV dataset by Bryant used. P: https://t.co/wEwER8goZt
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@AntonObukhov1
Anton Obukhov
1 year
Introducing πŸ›Ή RollingDepth πŸ›Ή β€” a universal monocular depth estimator for arbitrarily long videos! Our paper, β€œVideo Depth without Video Models,” delivers exactly that, setting new standards in temporal consistency. Check out more details in the thread 🧡
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