KeBingxin

Introducing Marigold 🌼 - a universal monocular depth estimator, delivering incredibly sharp predictions in the wild! Based on Stable Diffusion, it is trained with synthetic depth data only and excels in zero-shot adaptation to real-world imagery. Check it out: 🌐 Website: marigoldmonodepth.github.io 🤗 Hugging Face Space: huggingface.co/spaces/toshas/… 📄 Paper: arxiv.org/abs/2312.02145 👾 Code: github.com/prs-eth/marigo… The team: Bingxin Ke (@KBingxin), yours truly (@AntonObukhov1), Shengyu Huang (@ShengyHuang), Nando Metzger (@NandoMetzger), Rodrigo Caye Daudt (@rcdaudt), and Konrad Schindler. #ComputerVision #PRS #ETHZurich

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: research.nvidia.com/labs/toronto-a… Code and Models: github.com/nv-tlabs/lyra Paper: arxiv.org/abs/2509.19296

Unveiling BetterDepth — a plug-and-play diffusion-based refiner for zero-shot monocular depth estimation, compatible with many established depth prediction models. 📕 Paper: huggingface.co/papers/2407.17… 🧩 Other: TBA Fantastic collaboration between ETH Zurich and Disney Research|Studios, by Xiang Zhang (xiangz-0.github.io), @KBingxin, @chrysmun, @NandoMetzger, @AntonObukhov1, @MarkusGross63, Konrad Schindler, and Christopher Schroers.

Spice up your favorite SOTA monodepth network with a diffusion model! We introduce *BetterDepth*, a plug-and-play refiner for zero-shot monodepth estimation. Paper: huggingface.co/papers/2407.17…