Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers

Jinyang Liu, Wondmgezahu Teshome, Sandesh Ghimire, Mario Sznaier, Octavia Camps; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 23009-23018

Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately these methods face limitations in effectively solving puzzles with a large number of elements. In this paper we propose JPDVT an innovative approach that harnesses diffusion transformers to address this challenge. Specifically we generate positional information for image patches or video frames conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.