With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging—a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, encompassing 11,434 meticulously curated T-posed meshes adhered to a uniform skeleton topology. Capitalizing on this dataset, we introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNNbased methods in handling complex AI-generated meshes. Our approach integrates a Prior-Guided Skeleton Estimator (PGSE) module, which uses 2D skeleton joints to provide a preliminary 3D skeleton, and a Mesh-Skeleton Mutual Attention Network (MSMAN) that fuses skeleton features with 3D mesh features extracted by a U-shaped point transformer. This enables a coarse-to-fine 3D skeleton joint regression and a robust skinning estimation, surpassing previous methods in quality and versatility. This work not only remedies the dataset deficiency in rigging research but also propels the animation industry towards more efficient and automated character rigging pipelines.

Dam reservoirs play an important role in meeting sustainable development goals (SDGs) and global climate targets. However, particularly for small dam reservoirs, there is a lack of consistent data on their geographical location. To address this data gap, a promising approach is to perform automated dam reservoir extraction based on globally available remote sensing imagery. It can be considered as a fine-grained task of water body extraction, which involves extracting water areas in images and then separating dam reservoirs from natural water bodies. A straightforward solution is to extend the commonly used binary-class segmentation in water body extraction to multiclass. This, however, does not work well as there exists not much pixel-level difference of water areas between dam reservoirs and natural water bodies. We propose a novel deep neural network (DNN)-based pipeline that decomposes dam reservoir extraction into water body segmentation and dam reservoir recognition. Water bodies are first separated from background lands in a segmentation model and each individual water body is then predicted as either dam reservoir or natural water body in a classification model. For the former step, point-level metric learning (PLML) with triplets across images is injected into the segmentation model to address contour ambiguities between water areas and land regions. For the latter step, prior-guided metric learning (PGML) with triplets from clusters is injected into the classification model to optimize the image embedding space in a fine-grained level based on reservoir clusters. To facilitate future research, we establish a benchmark dataset with Earth imagery data and human-labeled reservoirs from river basins in West Africa and India. Extensive experiments were conducted on this benchmark in the water body segmentation task, dam reservoir recognition task, and the joint dam reservoir extraction task. Superior performance has been observed in the respective tasks when comparing our method with state-of-the-art approaches. The codes and datasets are available athttps://github.com/c8241998/Dam-Reservoir-Extraction.