arXiv Computer Vision
Sep 7

Object Concepts Emerge from Motion

The paper introduces a biologically inspired framework that learns object‑centric visual representations from raw videos without human annotations or camera calibration. By using motion boundaries detected via optical flow and clustering to create pseudo‑instance masks, the method supervises a single‑image encoder with pixel‑level pairwise metric learning. Training on 195 million pseudo‑labeled frames and expanding to 421 million frames through Motion‑Verified Self‑Training, the approach yields Swin‑based encoders that outperform or match supervised and self‑supervised baselines on tasks such as monocular depth estimation, 3D object detection, 3D occupancy prediction, and end‑to‑end planning.

By Boshi Li, Xiaohui Wang, Xiaoyang Wu, Zhichao Li, Ya Yang, Naiyan Wang
arXiv Computer Vision
1d ago

ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals

ORMA is a training‑free framework that reconstructs articulated 4D representations of animals from monocular videos by decoupling pose and shape. It uses predicted pose as a reference for optimization and generative 3D priors to refine shape, aligning the result with the SMAL+ parametric model. The method combines per‑frame pose estimates with globally consistent camera poses, and further refines the reconstruction using self‑supervised DINO correspondences and temporal consistency, achieving improved accuracy on the new PAW4D benchmark and diverse real‑world videos.

By Xuyi Hu, Francesco Palandra, Shangzhe Wu, Daniel Cremers, Riccardo Marin, Silvia Zuffi