Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The effect of the design of each component is still unclear. In addition, the iterative denoising process consumes considerable computational overhead, which is prohibitive for real-time scenarios such as virtual characters and humanoid robots. For this reason, we first conduct a comprehensive investigation into network architectures, training strategies, and inference process. Based on the profound analysis, we tailor each component for efficient high-quality human motion generation. Despite the promising performance, the tailored model still suffers from foot skating which is an ubiquitous issue in diffusion-based solutions. To eliminate footskate, we identify foot-ground contact and correct foot motions along the denoising process. By organically combining these well-designed components together, we present StableMoFusion, a robust and efficient framework for human motion generation. Extensive experimental results show that our StableMoFusion performs favorably against current state-of-the-art methods.

Gait recognition is a soft biotechnology to identify pedestrians observed from different camera views based on specific walking patterns. However, various dressing and wearing conditions bring great challenges to realistic gait recognition. Most existing methods take holistic gait silhouette as input and focus on local areas through horizontal strip division or attention map. We consider that this processing may contain mixed or incomplete information about multiple body parts so that gait information is misused or underutilized. In this paper, we propose a parsing-guided framework for gait recognition, named GaitParsing , which explores human semantic parsing to dissect human body into a set of specific and complete body parts. Correspondingly, a simple yet effective dual-branch feature extraction network is adopted to process holistic gait and distinct body parts. To maximize the use of highly discriminated gait frames, we propose a self-occlusion frame assessment to measure the self-occlusion in a gait sequence. Since there is no human parsing modality in current gait datasets, we further develop a general human parsing pipeline specifically tailored for gait datasets. This single training enables widespread application across various gait datasets. Extensive experiments with ablation analyses demonstrate competitive performance even in the most challenging conditions, e.g., Cloth-Changing (CC+5.9%). Especially, It is gratifying to see that our model can be easily applied to existing methods and significantly outperform the original architecture, even without much modification. Code is available at https://github.com/wzb-bupt/GaitParsing

Gait recognition is an emerging biometric technology for identifying pedestrians based on their unique walking patterns. In past gait recognition, global-based methods are inadequate to meet the growing demand for accuracy, while commonly used part-based methods provided coarse and inaccurate feature representation for specific body parts. Human parsing appears to be a better option for accurately representing specific and complete body parts in gait recognition. However, its practical application in gait recognition is often hindered by missing RGB modality, lack of annotated body parts, and difficulty in balancing parsing quantity and quality. To address this issue, we propose LandmarkGait, an accessible and alternative parsing-based solution for gait recognition. LandmarkGait introduces an unsupervised landmark discovery network to transform the dense silhouette into a finite set of landmarks with remarkable consistency across various conditions. By grouping landmarks subsets corresponding to distinct body part regions, following a reconstruction task and further refinement from high-quality input silhouettes, we can directly obtain fine-grained parsing results from original binary silhouettes in an unsupervised manner. Moreover, we also develop a multi-scale feature extractor that simultaneously captures global and parsing feature representations based on the integrity and flexibility of specific body parts. Extensive experiments demonstrate that our LandmarkGait can extract more stable features and exhibit significant performance improvement under all conditions, especially in various dressing conditions. Code is available at https://github.com/wzb-bupt/LandmarkGait.

Contrastive learning has shown impressive success in enhancing feature discriminability for various visual tasks in a self-supervised manner, but the standard contrastive paradigm (features+l2 normalization) has limited benefits when applied in domain adaptation. We find that this is mainly because the class weights (weights of the final fully connected layer) are ignored in the domain adaptation optimization process, which makes it difficult for features to cluster around the corresponding class weights. To solve this problem, we propose the simple but powerful Probabilistic Contrastive Learning (PCL), which moves beyond the standard paradigm by removing l2 normalization and replacing the features with probabilities. PCL can guide the probability distribution towards a one-hot configuration, thus minimizing the discrepancy between features and class weights. We conduct extensive experiments to validate the effectiveness of PCL and observe consistent performance gains on five tasks, i.e., Unsupervised/Semi-Supervised Domain Adaptation (UDA/SSDA), Semi-Supervised Learning (SSL), UDA Detection and Semantic Segmentation. Notably, for UDA Semantic Segmentation on SYNTHIA, PCL surpasses the sophisticated CPSL-D by 2% in terms of mean IoU with a much lower training cost (PCL: 1*3090, 5 days v.s. CPSL-D: 4*V100, 11 days). Code is available at https://github.com/ljjcoder/Probabilistic-Contrastive-Learning.