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Egocentric and exocentric perspectives of human action differ significantly, yet overcoming this extreme viewpoint gap is critical in augmented reality and robotics. We propose VIEWPOINTROSETTA, an approach that unlocks large-scale unpaired ego and exo video data to learn clip-level viewpoint-invariant video representations. Our framework introduces (1) a diffusion-based Rosetta Stone Translator (RST), which, leveraging a moderate amount of synchronized multi-view videos, serves as a translator in feature space to decipher the alignment between unpaired ego and exo data, and (2) a dual encoder that aligns unpaired data representations through contrastive learning with RST-based synthetic feature augmentation and soft alignment. To evaluate the learned features in a standardized setting, we construct a new cross-view benchmark using Ego-Exo4D, covering cross-view retrieval, action recognition, and skill assessment tasks. Our framework demonstrates superior cross-view understanding compared to previous view-invariant learning and ego video representation learning approaches, and opens the door to bringing vast amounts of traditional third-person video to bear on the more nascent first-person setting.
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Incorporating Scalability in Unsupervised Spatio-Temporal Feature Learning
Deep neural networks are efficient learning machines which leverage upon a large amount of manually labeled data for learning discriminative features. However, acquiring substantial amount of supervised data, especially for videos can be a tedious job across various computer vision tasks. This necessitates learning of visual features from videos in an unsupervised setting. In this paper, we propose a computationally simple, yet effective, framework to learn spatio-temporal feature embedding from unlabeled videos. We train a Convolutional 3D Siamese network using positive and negative pairs mined from videos under certain probabilistic assumptions. Experimental results on three datasets demonstrate that our proposed framework is able to learn weights which can be used for same as well as cross dataset and tasks.
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- Award ID(s):
- 1724341
- PAR ID:
- 10067976
- Date Published:
- Journal Name:
- IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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