We present WildHands, a method for 3D hand pose estimation in egocentric
images in the wild. This is challenging due to (a) lack of 3D hand pose
annotations for images in the wild, and (b) a form of perspective
distortion-induced shape ambiguity that arises in the analysis of crops around
hands. For the former, we use auxiliary supervision on in-the-wild data in the
form of segmentation masks & grasp labels in addition to 3D supervision
available in lab datasets. For the latter, we provide spatial cues about the
location of the hand crop in the camera's field of view. Our approach achieves
the best 3D hand pose on the ARCTIC leaderboard and outperforms FrankMocap, a
popular and robust approach for estimating hand pose in the wild, by 45.3% when
evaluated on 2D hand pose on our EPIC-HandKps dataset.

WildHands 是一种用于在野外的视角图像中进行 3D 手势估计的方法，通过使用野外数据的辅助监督以及在实验室数据集中提供的 3D 监督，以及关于相机视野中手势位置的空间线索，能够在 ARCTIC 排行榜上实现最佳 3D 手势姿态并在 EPIC-HandKps 数据集上超越 FrankMocap 方法达到 45.3％的 2D 手势姿态优势。

野外自觉图像中的 3D 手势姿势估计

3D Hand Pose Estimation in Egocentric Images in the Wild

In this paper, we tackle the challenge of face recognition in the wild, where
images often suffer from low quality and real-world distortions. Traditional
heuristic approaches-either training models directly on these degraded images
or their enhanced counterparts using face restoration techniques-have proven
ineffective, primarily due to the degradation of facial features and the
discrepancy in image domains. To overcome these issues, we propose an effective
adapter for augmenting existing face recognition models trained on high-quality
facial datasets. The key of our adapter is to process both the unrefined and
the enhanced images by two similar structures where one is fixed and the other
trainable. Such design can confer two benefits. First, the dual-input system
minimizes the domain gap while providing varied perspectives for the face
recognition model, where the enhanced image can be regarded as a complex
non-linear transformation of the original one by the restoration model. Second,
both two similar structures can be initialized by the pre-trained models
without dropping the past knowledge. The extensive experiments in zero-shot
settings show the effectiveness of our method by surpassing baselines of about
3%, 4%, and 7% in three datasets. Our code will be publicly available at
this https URL

在这篇论文中，我们致力于解决野外人脸识别的挑战，其中图片常常存在质量低下和真实世界的扭曲。通过提出一种适用于基于高质量数据集上训练的现有人脸识别模型的有效适配器，我们旨在克服传统启发式方法在处理低质量图片时的无效性。