Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
This addresses the challenge of accurately assembling puzzles with large numbers of elements, even with missing pieces, for applications in image and video processing.
The paper tackles the problem of solving image and video jigsaw puzzles with many elements by proposing JPDVT, which uses diffusion transformers to generate positional information for patches or frames based on visual content, achieving state-of-the-art performance on multiple datasets.
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.