1/6 Excited to share that our paper on model merging was accepted at ECCV 2026! 🎉 We introduce an efficient, decoder-free proxy that makes model selection faster, simpler and practical across vision tasks. 📄 arxiv.org/abs/2604.12935 🌐 europe.naverlabs.com/task-alignment 🧵👇

Check out our new paper in #CVPR2025! We test the limits of multi-teacher distillation to get one of the most versatile encoders yet. Mixing DINOv2 semantics 🦕, MASt3R multi-view reconstruction 📸📸...📸 and Human Mesh Recovery 🕺💃...🏌️‍♂️ Check our project page: europe.naverlabs.com/research/pub...

DUNE: Distilling a Universal Encoder from heterogenous 2D and 3D teachers

CVPR 2025 publication

europe.naverlabs.com

Yannis Kalantidis@skamalas.bsky.social · last yr.

1/ 📄 Paper 1: "DUNE: Distilling a UNiversal Encoder from Heterogeneous 2D and 3D Teachers" We propose DUNE: a ViT-based encoder distilled from multiple specialized 2D & 3D foundation models to unify visual tasks across 2D, 3D and human understanding.