Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction

Shannan Yan, Leqi Zheng, Keyu Lv, Jingchen Ni, Hongyang Wei, zjj, Guangting Wang, Jing Lyu, Chun Yuan, Fengyun Rao

Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction: 15 upvotes on Hugging Face Daily Papers, #9 of 25 papers on 2026-02-24. Day-by-day upvote history.

We study the task of establishing object-level visual correspondence across different viewpoints in videos, focusing on the challenging egocentric-to-exocentric and exocentric-to-egocentric scenarios. We propose a simple yet effective framework based on conditional binary segmentation, where an object query mask is encoded into a latent representation to guide the localization of the corresponding object in a target video. To encourage robust, view-invariant representations, we introduce a cycle-consistency training objective: the predicted mask in the target view is projected back to the source view to reconstruct the original query mask. This bidirectional constraint provides a strong self-supervisory signal without requiring ground-truth annotations and enables test-time training (TTT) at inference. Experiments on the Ego-Exo4D and HANDAL-X benchmarks demonstrate the effectiveness of our optimization objective and TTT strategy, achieving state-of-the-art performance. The code is available at https://github.com/shannany0606/CCMP.

Paper page on Hugging Face · arXiv

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