Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D hand mesh estimation network HandOccNet, that can fully exploits the information at occluded regions as a secondary means to enhance image features and make it much richer. To this end, we design two successive Transformer-based modules, called feature injecting transformer (FIT) and self- enhancing transformer (SET). FIT injects hand information into occluded region by considering their correlation. SET refines the output of FIT by using a self-attention mechanism. By injecting the hand information to the occluded region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh benchmarks that contain challenging hand-object occlusions. The codes are available in: https://github.com/namepllet/HandOccNet.
Recovering accurate 3D human pose and shape from in-the-wild crowd scenes is highly challenging and barely studied, despite their common presence. In this regard, we present 3DCrowdNet, a 2D human pose-guided 3D crowd pose and shape estimation system for in-the-wild scenes. 2D human pose estimation methods provide relatively robust outputs on crowd scenes than 3D human pose estimation methods, as they can exploit in-the-wild multi-person 2D datasets that include crowd scenes. On the other hand, the 3D methods leverage 3D datasets, of which images mostly contain a single actor without a crowd. The train data difference impedes the 3D methods' ability to focus on a target person in in-the-wild crowd scenes. Thus, we design our system to leverage the robust 2D pose outputs from off-the-shelf 2D pose estimators, which guide a network to focus on a target person and provide essential human articulation information. We show that our 3DCrowdNet outperforms previous methods on in-the-wild crowd scenes. We will release the codes.
Recovering expressive 3D human pose and mesh from in-the-wild images is greatly challenging due to the absence of the training data. Several optimization-based methods have been used to obtain pseudo-groundtruth (GT) 3D poses and meshes from GT 2D poses. However, they often produce bad ones with long running time because their frameworks are optimized on each sample only using 2D supervisions in a sequential way. To overcome the limitations, we present NeuralAnnot, a neural annotator that learns to construct in-the-wild expressive 3D human pose and mesh training sets. Our NeuralAnnot is trained on a large number of samples by 2D supervisions from a target in-the-wild dataset and 3D supervisions from auxiliary datasets with GT 3D poses in a parallel way. We show that our NeuralAnnot produces far better 3D pseudo-GTs with much shorter running time than the optimization-based methods, and the newly obtained training set brings great performance gain. The newly obtained training sets and codes will be publicly available.
Previous 3D human pose and mesh estimation methods mostly rely on only global image feature to predict 3D rotations of human joints (i.e., 3D rotational pose) from an input image. However, local features on the position of human joints (i.e., positional pose) can provide joint-specific information, which is essential to understand human articulation. To effectively utilize both local and global features, we present Pose2Pose, a 3D positional pose-guided 3D rotational pose prediction network, along with a positional pose-guided pooling and joint-specific graph convolution. The positional pose-guided pooling extracts useful joint-specific local and global features. Also, the joint-specific graph convolution effectively processes the joint-specific features by learning joint-specific characteristics and different relationships between different joints. We use Pose2Pose for expressive 3D human pose and mesh estimation and show that it outperforms all previous part-specific and expressive methods by a large margin. The codes will be publicly available.
Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We will release the codes. Demo video: https://www.youtube.com/watch?v=WB3nTnSQDII&t=7s&ab_channel=%EC%B5%9C%ED%99%8D%EC%84%9D
Analysis of hand-hand interactions is a crucial step towards better understanding human behavior. However, most researches in 3D hand pose estimation have focused on the isolated single hand case. Therefore, we firstly propose (1) a large-scale dataset, InterHand2.6M, and (2) a baseline network, InterNet, for 3D interacting hand pose estimation from a single RGB image. The proposed InterHand2.6M consists of \textbf{2.6M labeled single and interacting hand frames} under various poses from multiple subjects. Our InterNet simultaneously performs 3D single and interacting hand pose estimation. In our experiments, we demonstrate big gains in 3D interacting hand pose estimation accuracy when leveraging the interacting hand data in InterHand2.6M. We also report the accuracy of InterNet on InterHand2.6M, which serves as a strong baseline for this new dataset. Finally, we show 3D interacting hand pose estimation results from general images. Our code and dataset are available at https://mks0601.github.io/InterHand2.6M/.
Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image. The first weakness of these methods is an appearance domain gap problem, due to different image appearance between train data from controlled environments, such as a laboratory, and test data from in-the-wild environments. The second weakness is that the estimation of the pose parameters is quite challenging owing to the representation issues of 3D rotations. To overcome the above weaknesses, we propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose. The 2D human pose as input provides essential human body articulation information, while having a relatively homogeneous geometric property between the two domains. Also, the proposed system avoids the representation issues, while fully exploiting the mesh topology using a GraphCNN in a coarse-to-fine manner. We show that our Pose2Mesh outperforms the previous 3D human pose and mesh estimation methods on various benchmark datasets. The codes are publicly available https://github.com/hongsukchoi/Pose2Mesh_RELEASE.