Solving jigsaw puzzles, a long-standing challenge in both human cognition and artificial intelligence, has seen significant progress with modern computer vision techniques. In this paper, we introduce a diffusion-based framework for jigsaw puzzle reconstruction, leveraging denoising diffusion models to iteratively refine piece placements. Unlike prior methods that rely on anchored reference pieces and relative positioning, our approach directly regresses absolute positions, making it more flexible and generalizable. Additionally, we extend puzzle-solving beyond square pieces by incorporating polygonal partitions and employ DDIM for efficient inference. Our modular pipeline is adaptable to various puzzle formulations, and we demonstrate its effectiveness by achieving state-of-the-art performance on the JPwLEG benchmark.
Fotis, V, Romanelis, I, Moustakas, K & Ieee, NV 2025, Piecing It Together: A Unified Diffusion Framework for Jigsaw Puzzle Reconstruction. in 2025 International Conference On Visual Communications And Image Processing, Vcip. Ieee International Conference On Visual Communications And Image Processing, IEEE, 2025 Conference on Visual Communications and Image Processing-VCIP-Annual, Klagenfurt am Woerthersee, Austria, 1/12/25. https://doi.org/10.1109/VCIP67698.2025.11396878
Fotis, V., Romanelis, I., Moustakas, K., & Ieee, N. V. (2025). Piecing It Together: A Unified Diffusion Framework for Jigsaw Puzzle Reconstruction. In 2025 International Conference On Visual Communications And Image Processing, Vcip (Ieee International Conference On Visual Communications And Image Processing). IEEE. https://doi.org/10.1109/VCIP67698.2025.11396878
@inproceedings{406561a8fe8a43babad5bd9da5b57f19,
title = "Piecing It Together: A Unified Diffusion Framework for Jigsaw Puzzle Reconstruction",
abstract = "Solving jigsaw puzzles, a long-standing challenge in both human cognition and artificial intelligence, has seen significant progress with modern computer vision techniques. In this paper, we introduce a diffusion-based framework for jigsaw puzzle reconstruction, leveraging denoising diffusion models to iteratively refine piece placements. Unlike prior methods that rely on anchored reference pieces and relative positioning, our approach directly regresses absolute positions, making it more flexible and generalizable. Additionally, we extend puzzle-solving beyond square pieces by incorporating polygonal partitions and employ DDIM for efficient inference. Our modular pipeline is adaptable to various puzzle formulations, and we demonstrate its effectiveness by achieving state-of-the-art performance on the JPwLEG benchmark.",
keywords = "Deep Learning, Denoising Diffusion, Jigsaw Puzzles",
author = "Vlassis Fotis and Ioannis Romanelis and Konstantinos Moustakas and Ieee, \{[No Value]\}",
year = "2025",
doi = "10.1109/VCIP67698.2025.11396878",
language = "English",
isbn = "979-8-3315-6868-9",
series = "Ieee International Conference On Visual Communications And Image Processing",
publisher = "IEEE",
booktitle = "2025 International Conference On Visual Communications And Image Processing, Vcip",
note = "2025 Conference on Visual Communications and Image Processing-VCIP-Annual ; Conference date: 01-12-2025 Through 04-12-2025",
}