Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Jinyang Liu Wondmgezahu Teshome Sandesh Ghimire Mario Sznaier Octavia Camps
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.