Pose Embeddings: A Deep Architecture for Learning to Match Human Poses
CoRR(2015)
摘要
We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络