AHRNet: Attention and heatmap-based regressor for hand pose estimation and mesh recovery

Estimating 3D hand pose and recovering the full hand surface mesh from a single RGB image is a challenging task due to self-occlusions, viewpoint changes, and the complexity of hand articulations. In this paper, we propose a novel framework that combines an attention mechanism with heatmap regression to accurately and efficiently predict 3D joint locations and reconstruct the hand mesh. We adopt a pooling attention module that learns to focus on relevant regions in the input image to extract better features for handling occlusions, while greatly reducing the computational cost. The multi-scale 2D heatmaps provide spatial constraints to guide the 3D vertex predictions. By exploiting the complementary strengths of sparse 2D supervision and dense mesh regression, our method accurately reconstructs hand meshes with realistic details. Extensive experiments on standard benchmarks demonstrate that the proposed method efficiently improves the performance of 3D hand pose estimation and mesh recovery. The reproducible recipes are available at https://github.com/SDiannn/AHRNET-Heatmap

Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views

Developing gaze estimation models that generalize well to unseen domains and in-the-wild conditions remains a challenge with no known best solution. This is mostly due to the difficulty of acquiring ground truth data that cover the distribution of possible faces, head poses and environmental conditions that exist in the real world. In this work, we propose to train general gaze estimation models based on 3D geometry-aware gaze pseudo-annotations which we extract from arbitrary unlabelled face images, which are abundantly available in the internet. Additionally, we leverage the observation that head, body and hand pose estimation benefit from revising them as dense 3D coordinate prediction, and similarly express gaze estimation as regression of dense 3D eye meshes. We overcome the absence of compatible ground truth by fitting rigid 3D eyeballs on existing gaze datasets and design a multi-view supervision framework to balance the effect of pseudo-labels during training. We test our method in the task of gaze generalization, in which we demonstrate improvement of up to $30\%$ compared to state-of-the-art when no ground truth data are available, and up to $10\%$ when they are. The project material will become available for research purposes.Comment: 13 pages, 12 figure