Personalizing Human Video Pose Estimation
This addresses the challenge of accurate pose estimation for individuals in videos, offering a personalized solution that enhances performance in applications like video analysis and human-computer interaction, though it is incremental as it builds on existing ConvNet methods.
The paper tackles the problem of improving pose estimation in long videos by personalizing a ConvNet pose estimator to adapt to a person's unique appearance, resulting in substantial improvements over generic methods and outperforming state-of-the-art approaches on standard and new benchmarks.
We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporally local frames; (ii) we develop an occlusion aware self-evaluation model that is able to automatically select the high-quality and reject the erroneous additional annotations; and (iii) we demonstrate that these high-quality annotations can be used to fine-tune a ConvNet pose estimator and thereby personalize it to lock on to key discriminative features of the person's appearance. The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet. Our method outperforms the state of the art (including top ConvNet methods) by a large margin on two standard benchmarks, as well as on a new challenging YouTube video dataset. Furthermore, we show that training from the automatically generated annotations can be used to improve the performance of a generic ConvNet on other benchmarks.