CDG-MAE: Learning Correspondences from Diffusion Generated Views

• URL: http://arxiv.org/abs/2506.18164v1
• Date: Sun, 22 Jun 2025 20:40:11 GMT
• Title: CDG-MAE: Learning Correspondences from Diffusion Generated Views
• Authors: Varun Belagali, Pierre Marza, Srikar Yellapragada, Zilinghan Li, Tarak Nath Nandi, Ravi K Madduri, Joel Saltz, Stergios Christodoulidis, Maria Vakalopoulou, Dimitris Samaras,
• Abstract summary: CDG-MAE is a novel MAE-based self-supervised method that uses diverse synthetic views generated from static images.<n>These generated views exhibit substantial changes in pose and perspective, providing a rich training signal.
• Score: 19.24402848656637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning dense correspondences, critical for application such as video label propagation, is hindered by tedious and unscalable manual annotation. Self-supervised methods address this by using a cross-view pretext task, often modeled with a masked autoencoder, where a masked target view is reconstructed from an anchor view. However, acquiring effective training data remains a challenge - collecting diverse video datasets is difficult and costly, while simple image crops lack necessary pose variations. This paper introduces CDG-MAE, a novel MAE-based self-supervised method that uses diverse synthetic views generated from static images via an image-conditioned diffusion model. These generated views exhibit substantial changes in pose and perspective, providing a rich training signal that overcomes the limitations of video and crop-based anchors. We present a quantitative method to evaluate local and global consistency of generated images, discussing their use for cross-view self-supervised pretraining. Furthermore, we enhance the standard single-anchor MAE setting to a multi-anchor strategy to effectively modulate the difficulty of pretext task. CDG-MAE significantly outperforms state-of-the-art MAE methods reliant only on images and substantially narrows the performance gap to video-based approaches.

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