Pose-guided person image generation that aims to transfer the pose of a given person to a target pose has recently received lots of research attention. Due to the spatial misalignment and occlusions of different local body parts by pose variations, this task is still challenging especially in maintaining high-fidelity textures and body structures in generated images. Besides, most works also suffer from the limited number of texture styles in the given person datasets, restricting the diversity of generated persons' appearances. To solve these problems, we design a Kernel-based Texture-Fusion Joint Refinement Network (TFJR-Net) to jointly refine the structure and texture information of generated images. First, we leverage a bone-map representation to guide the generation of human parsing maps, which has more structure priors and richer context information than traditional key-point maps, thus reduce the uncertainty of generated body structures. Next, a Texture-Kernel Injection Normalization module (TKIN) is proposed to inject the per-region texture-kernel into the corresponding semantic region from the human parsing map, which decouples the texture and shape information, and also preserves fine-grained features for complex textures. Furthermore, we are the first to introduce external texture patterns outside of the dataset in human semantic regions such as the upper clothes. We fuse the two texture domains in a shared texture space through our designed texture-fusion TKIN modules. Extensive experiments are conducted on the Deepfashion dataset, with the DTD dataset as an external texture source. The experimental results demonstrate the superiority of our proposed method in generating persons of better textures and structures than state-of-the-art works, and also show the generalization ability of our proposed method to absorb diversified external textures for generating person images. The source codes are available athttps://github.com/pilgrim00/TKIN.

Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and retraining when dealing with novel classes never seen before. On the other hand, we humans can quickly recognize new classes by looking at a few samples, which motivates the recent popularity of few-shot learning (FSL) in machine learning communities. Most current FSL approaches work on 2D image domain, however, its implication in 3D perception is relatively under-explored. Not only needs to recognize the unseen examples as in 2D domain, 3D few-shot learning is more challenging with unordered structures, high intra-class variances and subtle inter-class differences. Moreover, different architectures and learning algorithms make it difficult to study the effectiveness of existing 2D methods when migrating to the 3D domain.In this work, for the first time, we perform systematic and extensive studies of recent 2D FSL and 3D backbone networks for benchmarking few-shot point cloud classification, and we suggest a strong baseline and learning architectures for 3D FSL. Then, we propose a novel plug-and-play component called Cross-Instance Adaptation (CIA) module, to address the high intra-class variances and subtle inter-class differences issues, which can be easily inserted into current baselines with significant performance improvement. Extensive experiments on two newly introduced benchmark datasets, ModelNet40-FS and ShapeNet70-FS, demonstrate the superiority of our proposed network for 3D FSL.

Point cloud instance segmentation has achieved huge progress with the emergence of deep learning. However, these methods are usually data-hungry with expensive and time-consuming dense point cloud annotations. To alleviate the annotation cost, unlabeled or weakly labeled data is still less explored in the task. In this paper, we introduce the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision. To be specific, our SPIB architecture involves a two-stage learning procedure. For stage one, a bounding box proposal generation network is trained under a semi-supervised setting with perturbation consistency regularization (SPCR). The regularization works by enforcing an invariance of the bounding box predictions over different perturbations applied to the input point clouds, to provide self-supervision for network learning. For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module. Moreover, we introduce a novel occupancy ratio guided refinement module to refine the instance masks. Extensive experiments on the challenging ScanNet v2 dataset demonstrate our method can achieve competitive performance compared with the recent fully-supervised methods.

Point cloud instance segmentation is steadily improving with the development of deep learning. However, current progress is hindered by the expensive cost of collecting dense point cloud labels. To this end, we propose the first semi-supervised point cloud instance segmentation architecture, which is called semi-supervised point cloud instance segmentation with perturbation consistency regularization (SPCR). It is capable to alleviate the data-hungry bottleneck of existing strongly supervised methods. Specifically, SPCR enforces an invariance of the predictions over different perturbations applied to the input point clouds. We firstly introduce various perturbation schemes on inputs to force the network to be robust and easily generalized to the unseen and unlabeled data. Further, perturbation consistency regularization is then conducted on predicted instance masks from various transformed inputs to provide self-supervision for network learning. Extensive experiments on the challenging ScanNet v2 dataset demonstrate our method can achieve competitive performance compared with the state-of-the-art of fully supervised methods.