@NoamRot
Joined April 2009
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  • Following670
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🎬 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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Yftah Ziser@YftahZOct 22
1/4 We trained an 8B safety classifier on just 1,275 carefully annotated prompts. It beat GPT-5.4, Claude Sonnet 4.6, and leading open-weight guardrails trained on far more data, on average across 5 safety benchmarks. Quality > quantity.
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We build ~200K video–motion pairs across diverse rigs. A major part is “Objaverse in Video”: a video editor places existing animated asset renders in suitable scenes, creating data at scale for arbitrary rigs. We add human and robot data.
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For more details, check out the paper! Project page: research.nvidia.com/labs/par… Paper: arxiv.org/abs/2610.28632 Big shoutout to my collaborators! @YoadTewel @ChenTessler @HaggaiMaron @GalChechik @orlitany @yoni_kasten @AtzmonYuval at @nvidia Research
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Uri Berger@uriberger88Oct 13
Do vision-language models look at the image regions they describe? In our new paper, we present Self-Saliency, a method for aligning visual attention with reasoning text. research.nvidia.com/labs/par… w/ @GalChechik @DalalGal

Self-Saliency: Look Where You Say You're Looking

Grounding a VLM's own reasoning chain, and rewarding it for attending to the regions it just described.

research.nvidia.com
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Saar Huberman
@HubermanSaarOct 4
Excited to share our work, “Token-to-Token Alignment of Text Embeddings for Semantic Blending,” accepted as a NeurIPS 2026 Spotlight 🌟 Can we directly interpolate between semantic concepts? Text embedding spaces already encode a continuous semantic structure, the missing piece is alignment. Once aligned, simple interpolation becomes meaningful, naturally enabling multiple applications. Joint work with @MokadyRon @OPatashnik @DanielCohenOr1
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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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To Pareto and beyond! 🚀
Shelly Golan@Shelly_Golan1Sep 30
Excited to share that our paper "ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control" has been accepted to @NeurIPSConf as an oral! 🎉 ParetoSlider enables continuous inference-time control over competing reward trade-offs with a single diffusion model, without retraining or maintaining multiple checkpoints. Huge thanks to the great team @mikikiFin, @ArielBAMath, @YotamNitzan, @OPatashnik Paper: arxiv.org/abs/2604.20816 Project page: shelley-golan.github.io/Pare… Code: github.com/Shelley-Golan/Par…

ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a single scalar reward. When multiple criteria...

arxiv.org
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Roy Ganz@roy_ganzAug 26
1/11 Can a stronger coding model rescue a run started by a cheaper one? Can a cheaper model continue a stronger model's work - retaining quality at lower cost? The two directions behave very differently... Check out our new paper: The Handoff Tax 🧵 📄 arxiv.org/abs/2608.24358
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Dvir Samuel@dvir_samuelAug 16
🥳 Excited to share UniProbe: A learnable, token-level hallucination detection for large VLMs 🔎 UniProbe looks inside a VLM’s internals to identify hallucinated response tokens and mitigate them before they reach the user. 📉 55% fewer hallucinations ⚡ Just 1.06× latency
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Dvir Samuel@dvir_samuelJul 6
I'm going to present Fast-AR at #ICML26 tomorrow (Tuesday) @ 2pm - poster #1204! Come and say hi 👋 If you're around this week, feel free to DM me. Project page: dvirsamuel.github.io/fast-au… Details below ⬇️
Dvir Samuel@dvir_samuelFeb 3
🚀 Excited to share our new paper: “Fast Autoregressive Video Diffusion & World Models with Temporal Cache Compression & Sparse Attention.” We address attention bottlenecks in auto-regressive video diffusion, enabling ×5–×10 speedup and constant memory over long rollouts.
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Tomer Borreda@TomerBorredaJun 21
Excited to share that my first PhD paper, RadarGen, got accepted to #ECCV2026! 🎉 Let’s talk about radar simulation in Malmö 🇸🇪
Tomer Borreda@TomerBorreda22 Dec 2025
📢 RadarGen: Automotive Radar Point Cloud Generation from Cameras Can we generate realistic radar point clouds solely from camera images? 🚗📡 We introduce RadarGen, a diffusion-based framework that synthesizes radar returns aligned with visual scenes. radargen.github.io
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Amir Mann@mann_amir_Jun 12
Glad to share the main research of my master's degree, VideoMDM! Please go check it out! videomdm.github.io

VideoMDM — 3D Human Motion Generation from 2D Supervision

A diffusion-based framework that trains 3D human motion priors from monocular video, without any 3D ground truth.

videomdm.github.io
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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Technion Israel
@TechnionLiveJun 11
A new technology developed at the Technion could significantly expand access to AI-powered video creation Called Time to Move (TTM), the system enables users to create and precisely control realistic AI-generated video clips using simple mouse movements. Unlike previous approaches, TTM requires no retraining, no additional computational cost, and can be integrated directly into existing video-generation models. Developed by Dr. @orlitany , Prof. Ron Kimmel, and students Asaf Singer, Noam Rotstein, and Amir Mann from the Henry and Marilyn Taub Faculty of Computer Science, TTM was presented at @iclr_conf 2026, one of the world's premier conferences in deep learning and artificial intelligence.
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Exciting work tying image manipulation and brain modeling 🚀
Navve wasserman@NavveWJun 9
How can we tell whether a brain region causally represents a visual concept, rather than merely correlating with it? Introducing BrainCause, a framework combining generative and brain models to create controlled stimuli and causally test neural representations. More below 🧠👇
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Yoad Tewel@YoadTewelJun 9
🎉 Happy to share our latest work: Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching (accepted to #ICML2026)! TL;DR: We train image and video editing models without any paired data. No ground-truth edit data, no external reward models.
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