@danielgilo1
Joined December 2025
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  • Followers27
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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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Guy Bar-Shalom@GuyBarSh25m
[1/5] 🍋 New paper: LEMON, a misbehavior monitor that transfers zero-shot to LLMs it has never seen. It flags hallucinations & jailbreaks online, token by token, from attention maps, with <10% overhead. w/ @megamor2 @ffabffrasca @HaggaiMaron 🌐 barsguy.github.io/LEMON_proj… 🧵👇
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Noam Rotstein
@NoamRotOct 21
🎬 Excited to share our paper: Joints, Camera, Action! (JCA) JCA generates video & 3D motion together for any rig! Video models generate motion you can watch. But what if you could use that motion to animate a 3D asset, with motion that responds to its surroundings? research.nvidia.com/labs/par… 🧵
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Or Litany@orlitanyOct 2
Meet DyRAD: Radar NVS for dynamic driving scenes. 📡🚗 Radar directly senses motion through Doppler, but prior radar NVS methods ignore this signal where it matters most: dynamic scenes. 🌐 dyrad-nvs.github.io 📄 arxiv.org/abs/2609.39841 💻 github.com/Dyrad-NVS/DyRAD 🧵
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Or Litany@orlitanySep 10
If you think differentiable rendering is all you need to animate your 3DGS avatars/assets, think again! At ECCV? We invite you to come re-think together with us at poster #135 happening today at 10:30 #ECCV2026 #3DGS
Or Litany@orlitanyMar 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/Spectral… 🧵
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Update: FlowBender code is public 🚀 ✅ Depth → RGB ✅ Edge map → RGB ✅ Super-resolution ✅ JPEG restoration ✅ 3D texturing Try it: github.com/DanielGilo/FlowBe… Project page: flow-bender.github.io/

GitHub - DanielGilo/FlowBender: Official code for the FlowBender paper.

Official code for the FlowBender paper. . Contribute to DanielGilo/FlowBender development by creating an account on GitHub.

github.com
Daniel Gilo@danielgilo1Jun 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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Guy Bar-Shalom@GuyBarShApr 15
New blogpost out 📃 "Detecting LLM Misbehaviors from the Inside Out with Deep Learning on Structured Data" (ffabffrasca.substack.com/p/d…) [1/8]
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Finally, is FlowBender just automated gradient guidance? We show the answer is No: 80% of the learned correction is orthogonal to the error gradient. The gradient is utilized — but as a feature driving a non-linear policy, not a scalar weight. No guidance scheme can express this.
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Thanks for reading! We'd love to hear your thoughts — questions, feedback, and ideas all welcome. 📄 Paper: arxiv.org/pdf/2606.20404 🌐 Project page: flow-bender.github.io @orlitany @s_elflein @IdoSobol
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Or Litany@orlitanyJun 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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Or Litany@orlitanyJun 7
This morning @CVPR: [poster #35] we explain how to — consistent edits of multiview Images without training @danielgilo1 and I will be there to walk you through the method
Or Litany@orlitanyFeb 25
Excited to share our new paper, accepted to CVPR 2026: Instruct-Mix2Mix! 📢 We tackle the challenge of sparse multi-view editing: modifying a scene given only a few images (e.g., 4-8) via text instructions. The code is available now. Project page: danielgilo.github.io/instruc… 🧵
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Or Litany@orlitanyJun 6
This afternoon @CVPR: Realiz3D [poster #526] Come learn how to make image diffusion models 3D aware without leaking synthetic appearance.
Ido Sobol@IdoSobolApr 3
Tired of 3D generations that look synthetic? They don’t have to. Excited to share that Realiz3D is accepted to #CVPR2026 🎉 A framework for training diffusion models that are 3D-consistent, controllable, and photorealistic. 🔗 Project page: idosobol.github.io/realiz3d/ 🎥 Teaser ↓
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Ido Sobol@IdoSobolApr 3
Tired of 3D generations that look synthetic? They don’t have to. Excited to share that Realiz3D is accepted to #CVPR2026 🎉 A framework for training diffusion models that are 3D-consistent, controllable, and photorealistic. 🔗 Project page: idosobol.github.io/realiz3d/ 🎥 Teaser ↓
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Or Litany@orlitanyFeb 25
Excited to share our new paper, accepted to CVPR 2026: Instruct-Mix2Mix! 📢 We tackle the challenge of sparse multi-view editing: modifying a scene given only a few images (e.g., 4-8) via text instructions. The code is available now. Project page: danielgilo.github.io/instruc… 🧵
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