Rotem Shalev-Arkushin
@rotemsh3
CS PhD student @ Tel-Aviv University
Excited to introduce our new work: ImageRAG 🖼️✨ rotem-shalev.github.io/ImageRAG We enhance off-the-shelf generative models with Retrieval-Augmented Generation (RAG) for unknown concept generation, using a VLM-based approach that’s easy to integrate with new & existing models! [1/3]
![rotemsh3's tweet image. Excited to introduce our new work: ImageRAG 🖼️✨
rotem-shalev.github.io/ImageRAG
We enhance off-the-shelf generative models with Retrieval-Augmented Generation (RAG) for unknown concept generation, using a VLM-based approach that’s easy to integrate with new & existing models!
[1/3]](https://pbs.twimg.com/media/GkAFzR5XsAAXTGB.jpg)
1/ Can we teach a motion model to "dance like a chicken" Or better: Can LoRA help motion diffusion models learn expressive, editable styles without forgetting how to move? Led by @HSawdayee, @chuan_guo92603, we explore this in our latest work. 🎥 haimsaw.github.io/LoRA-MDM/ 🧵👇
Really impressive results for human-object interaction. They use a two-phase process where they optimize the diffusion noise, instead of the motion itself, to get to sub-centimeter precision while staying on manifold 🧠 HOIDiNi - hoidini.github.io
Excited to share that our new work, Be Decisive, has been accepted to SIGGRAPH! We improve multi-subject generation by extracting a layout directly from noise, resulting in more diverse and accurate compositions. Website: omer11a.github.io/be-decisive/ Paper: arxiv.org/abs/2505.21488
Excited to share that "IP-Composer: Semantic Composition of Visual Concepts" got accepted to #SIGGRAPH2025!🥳 We show how to combine visual concepts from multiple input images by projecting them into CLIP subspaces - no training, just neat embedding math✨ Really enjoyed working…
🔔just landed: IP Composer🎨 semantically mix & match visual concepts from images ❌ text prompts can't always capture visual nuances ❌ visual input based methods often need training / don't allow fine grained control over *which* concepts to extract from our input images So👇
🔔Excited to announce that #AnyTop has been accepted to #SIGGRAPH2025!🥳 ✅ A diffusion model that generates motion for arbitrary skeletons ✅ Using only a skeletal structure as input ✅ Learns semantic correspondences across diverse skeletons 🌐 Project: anytop2025.github.io/Anytop-page
RefVNLI Towards Scalable Evaluation of Subject-driven Text-to-image Generation
How well LLMs are memorizing obscure details from scientific papers? I created a benchmark for that! Full code, dataset and data creation method included. tl;dr GPT4.5 is a major jump in scientific facts memorization. Thread below 👇
🚀 Meet DiP: our newest text-to-motion diffusion model! ✨ Ultra-fast generation ♾️ Creates endless, dynamic motions 🔄 Seamlessly switch prompts on the fly Best of all, it's now available in the MDM codebase: github.com/GuyTevet/motio… [1/3]
🔔🔔Thrilled to share #MoMo [@SIGGRAPHAsia 2024 🥳🎉]: Exploring the attention space of #MotionDiffusionModels. Our training-free method enables cool applications like this motion transfer 🐒🐒. monkeyseedocg.github.io