Or Litany

@orlitany

Assistant professor @TechnionLive and Sr. Research Scientist @NVIDIA | (novel) views are my own

orlitany.github.io

Joined March 2018

Or Litany
@orlitany
Jun 29
Fantastic work -- principled and works great. Congrats @OrPerel and team!
Or Perel
@OrPerel
Jun 29
🎉 Excited to introduce TRON, a relighting framework for 3D captures. 💡TRON pairs a neural renderer with 3D Gaussian reconstructions, achieving realistic quality, with 3D, material, & lighting control at interactive frame rates. arxiv.org/abs/2606.11314 research.nvidia.com/labs/sil/proje…
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Or Litany
@orlitany
Jun 20
SpectralSplats is accepted at #ECCV2026! 🎉 Tracking 3DGS across frames is harder than it looks — appearance losses break the moment the pose drifts. Spectral moments keep it robust. @eccvconf
Or Litany
@orlitany
Mar 26
1/9 Excited to share SpectralSplats! 📢 Given a 3DGS asset + target video, we deform it to match the video via differentiable rendering. Appearance-based tracking fails when the initial pose is even slightly off. Our spectral loss stays robust. 🔗 avigailco.github.io/SpectralSplats 🧵
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Or Litany
@orlitany
Jun 20
Excited that RadarGen is accepted to #ECCV2026! Looking forward to chatting radar simulation in Malmö — let’s push this underexplored topic forward. @eccvconf
Or Litany
@orlitany
Dec 22, 2025
🚗📡Radar is the unsung hero of AV perception: widespread in cars, yet overlooked in simulation. Introducing RadarGen: Realistic radar synthesis from cameras using diffusion. Massive kudos to my fantastic team at @TechnionLive and @NVIDIAAI radargen.github.io
Or Litany
@orlitany
Jun 19
📢 New paper: FlowBender. Conditional generators drift from their own conditioning. The usual fix: tune guidance & pray 🙏 💡We train them to self-correct from their own error +4.5 dB on 3D texturing, +4.9 dB on SR. Give it a spin and bend away👇 flow-bender.github.io
Daniel Gilo
@danielgilo1
Jun 19
Conditional diffusion/flow models often produce outputs inconsistent with the very signal conditioning them. The error is easily measurable, yet models are never trained to act on it. In FlowBender (now on arXiv), we train the model to correct its own errors. 🧵
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Or Litany
@orlitany
Jun 12
Excited to share our new paper: VideoMDM 📢 We propose a principled framework for training 3D motion diffusion models (e.g. MDM), using only 2D supervision from monocular videos -- no 3D ground truth required. Project: videomdm.github.io Paper: arxiv.org/abs/2606.13364 🧵
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