🎊 Excited to share our latest work: “DGD: Dynamic 3D Gaussians Distillation”! 🚀 To appear at #ECCV2024. DGD distills 2D semantic features into dynamic 3D Gaussians, enabling the reconstruction and semantic segmentation of dynamic objects in 3D using only a user click. 🔗

[1/6] 🎬 New paper: Story2Board We guide diffusion models to generate consistent, expressive storyboards--no training needed. By mixing attention-aligned tokens across panels, we reinforce character identity without hurting layout diversity. 🌐 daviddinkevich.github.io/Story2Board

[1/10] 🤔 What if you wanted to generate a 3D model of a “Bolognese dog” 🐕 or a “Labubu doll” 🧸? Try it with existing text-to-3D models → they collapse. Why? These concepts are rare or new, and the model has never seen them. 🚀 Our solution: MV-RAG See details below ⬇️