Sigal Raab

🔔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

{1/8} 🧵 When you click a link, have you ever wondered: “Which webpage is actually important?” Google answered that with PageRank—treating the web as a Markov chain. Now imagine doing the same… but for transformer attention.👇 🔗 yoterel.github.io/attention_chai…

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

🏃Today at CVPR!!! 📅🕐 13:00–17:00, 📍Room 110B 💃

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…

Excited to share that "TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space" got accepted to SIGGRAPH 2025! It tackles disentangling complex visual concepts from as little as a single image and re-composing concepts across multiple images into a coherent…

🔔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👇

Ever stared at a set of shapes and thought: 'These could be something… but what?' Designed for visual ideation, PiT takes a set of concepts and interprets them as parts within a target domain, assembling them together while also sampling missing parts. eladrich.github.io/PiT/

📢 Deadline extended! 📢 You now have an extra week to submit! New deadline: March 19. Want to submit? Find all the details here: humogen.github.io 🚀

Vectorization into a neat SVG!🎨✨ Instead of generating a messy SVG (left), we produce a structured, compact representation (right) - enhancing usability for editing and modification. Accepted to #CVPR2025 !

“Tight Inversion” uses an IP-Adapter during DDIM inversion to preserve the original image better when editing. arxiv.org/abs/2502.20376

Human Motion people - There is still a path to Nashville...Just saying @CVPR