Doduo: 教師なし意味認識フローによる密な視覚的対応学習
Doduo: Learning Dense Visual Correspondence from Unsupervised Semantic-Aware Flow
動的なシーンでも画像間の密な対応を教師なしで学習する手法Doduoを提案し、関節推定やゼロショット目標条件付き操作に応用した。
著者: Zhenyu Jiang, Hanwen Jiang, Yuke Zhu
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
Dense visual correspondence plays a vital role in robotic perception. This work focuses on establishing the dense correspondence between a pair of images that captures dynamic scenes undergoing substantial transformations. We introduce Doduo to learn general dense visual correspondence from in-the-wild images and videos without ground truth supervision. Given a pair of images, it estimates the dense flow field encoding the displacement of each pixel in one image to its corresponding pixel in the other image. Doduo uses flow-based warping to acquire supervisory signals for the training. Incorporating semantic priors with self-supervised flow training, Doduo produces accurate dense correspondence robust to the dynamic changes of the scenes. Trained on an in-the-wild video dataset, Doduo illustrates superior performance on point-level correspondence estimation over existing self-supervised correspondence learning baselines. We also apply Doduo to articulation estimation and zero-shot goal-conditioned manipulation, underlining its practical applications in robotics. Code and additional visualizations are available at https://ut-austin-rpl.github.io/Doduo
