Zurich, Switzerland
Joined May 2013
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Dominik Narnhofer retweeted
Our recent finding on Diffusion Alignment: a reward model in pixel space can be easily transferred to score noisy diffusion latents directly — at small finetuning cost, via stitching.
This makes Faster & Better for both Training & Inference Alignment.
Meet StitchVM👇
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Want to leverage the power of SOTA 3D models like VGGT & Video LDMs for 3D generation? Now you can! 🚀
Introducing VIST3A — we stitch pretrained video generators to 3D foundation models and align them via reward finetuning.
📄 arxiv.org/abs/2510.13454
🌐 gohyojun15.github.io/VIST3A
Dominik Narnhofer retweeted
🚀 Just released: FLAIR – a new training-free approach to solving inverse problems using flow-matching models!
🎯 Try it live: huggingface.co/spaces/prs-et…
📚 Learn more: inverseflair.github.io/
🚀 Excited to Share: Solving Inverse Problems with FLAIR! 🚀
We present FLAIR, a novel framework for inverse problems using generative flow-based models.
Project Page 🚀 : inverseflair.github.io
arXiv 📜 : arxiv.org/abs/2506.02680
Demo 🤗 : huggingface.co/spaces/prs-et…
Dominik Narnhofer retweeted
RollingDepth rolls into Nashville for #CVPR2025! 🎸
Dominik Narnhofer retweeted
We present Thera🔥: 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.
Dominik Narnhofer retweeted
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: arxiv.org/abs/2501.19200
