Multi-behavior recommender systems have demonstrated their effectiveness in mitigating issues such as data sparsity by incorporating auxiliary behaviors into the target behavior. However, existing multi-behavior recommendation approaches typically take one of two directions: (1) fusing behavior-specific preference features from various behavior interaction graphs explicitly or implicitly for recommendation; or (2) utilizing behavior-unified preference features from the unified interaction graph for recommendation or to initialize features for subsequent modeling. These methods fail to exploit the integration of behavior-unified global and behavior-specific local preference features, resulting in incomplete preference modeling. To address this issue, in this work, we propose a novel method calledUnifyingGlobal andLocalPreferences (UGLP) for multi-behavior recommendation. In UGLP, we design a behavior feature fusion network that consists of global and local fusion modules for comprehensive and fine-grained user preferences. The global fusion module performs graph convolution on behavior-unified global and behavior-specific local interaction graphs to obtain behavior-unified and behavior-specific features. The behavior-unified and behavior-specific features are then fused into globally fused features via a gating network. The local fusion module then performs cross-behavior fusion on these globally fused features via another gating network. We introduce a contrastive learning module to promote preference alignment and knowledge transfer from auxiliary behaviors to the target behavior. Additionally, we incorporate a GCN refinement module to adjust the fused features to ensure that both global and local user preferences are learned. Experimental results on three real-world datasets verify that our method is able to surpass various state-of-the-art models. For instance, our method outperforms the best baseline by an average of 16.43% and 14.36% in terms of HR@10 and NDCG@10, respectively.

Unsupervised domain adaptation (UDA) has emerged as a promising technique for transferring knowledge from a labeled domain to an unlabeled domain. However, existing UDA methods are severely constrained by data privacy and semantic inconsistencies. To alleviate these limitations, this work challenges the Source-Free Open-Set Domain Adaptation (SF-OSDA), where the pre-trained source model is directly leveraged on the open target domain for adaptation. For this purpose, we introduce the novel Dynamic Target Distribution Estimation (DTDE) method, which effectively performs known classification and unknown separation through self-supervised learning with prototypes. To construct known prototypes, a self-adaptive sampling strategy is employed to consider the category disparity. For unknown prototypes, we utilize a self-splitting and excluding principle to bypass the unknown semantics problem. Specifically, self-splitting is to evaluate the overall clustering distribution of the target domain. By excluding clusters resembling known prototypes, the remaining cluster centroids can serve as unknown prototypes. The superiority of our approach is validated across multiple benchmarks. Remarkably, DTDE outperforms the best competitor by 7.6% on the VisDA dataset.

Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios. In this paper, we focus on a new task, i.e., Multi-Garment Virtual Dressing, and we propose a novel Any-Dressing method for customizing characters conditioned on any combination of garments and any personalized text prompts. AnyDressing comprises two primary networks named GarmentsNet and DressingNet, which are respectively dedicated to extracting detailed clothing features and generating customized images. Specifically, we propose an efficient and scalable module called Garment-Specific Feature Extractor in GarmentsNet to individually encode garment textures in parallel. This design prevents garment confusion while ensuring network efficiency. Meanwhile, we design an adaptive Dressing-Attention mechanism and a novel Instance-Level Garment Localization Learning strategy in DressingNet to accurately inject multi-garment features into their corresponding regions. This approach efficiently integrates multi-garment texture cues into generated images and further enhances text-image consistency. Additionally, we introduce a Garment-Enhanced Texture Learning strategy to improve the fine-grained texture details of garments. Thanks to our well-craft design, Any-Dressing can serve as a plug-in module to easily integrate with any community control extensions for diffusion models, improving the diversity and controllability of synthesized images. Extensive experiments show that AnyDressing achieves state-of-the-art results.

The trend of employing training-free methods for point cloud recognition is becoming increasingly popular due to its significant reduction in computational resources and time costs. However, existing approaches are limited as they typically extract either geometric or semantic features. To address this limitation, we are the first to propose a novel training-free method that integrates both geometric and semantic features. For the geometric branch, we adopt a non-parametric strategy to extract geometric features. In the semantic branch, we leverage a model aligned with text features to obtain semantic features. Additionally, we introduce the GFE module to complement the geometric information of point clouds and the MFF module to improve performance in few-shot settings. Experimental results demonstrate that our method outperforms existing state-of-the-art training-free approaches on mainstream benchmark datasets, including ModelNet and ScanObiectNN.

Embodied intelligence requires precise reconstruction and rendering to simulate large-scale real-world data. Although 3D Gaussian Splatting (3DGS) has recently demonstrated high-quality results with real-time performance, it still faces challenges in indoor scenes with large, textureless regions, resulting in incomplete and noisy reconstructions due to poor point cloud initialization and underconstrained optimization. Inspired by the continuity of signed distance field (SDF), which naturally has advantages in modeling surfaces, we propose a unified optimization framework that integrates neural signed distance fields (SDFs) with 3DGS for accurate geometry reconstruction and real-time rendering. This framework incorporates a neural SDF field to guide the densification and pruning of Gaussians, enabling Gaussians to model scenes accurately even with poor initialized point clouds. Simultaneously, the geometry represented by Gaussians improves the efficiency of the SDF field by piloting its point sampling. Additionally, we introduce two regularization terms based on normal and edge priors to resolve geometric ambiguities in textureless areas and enhance detail accuracy. Extensive experiments in ScanNet and ScanNet++ show that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis. Project page: https://xhd0612.github.io/GaussianRoom.github.io/

Drawing freehand sketches of mechanical components on multimedia devices for AI-based engineering modeling has become a new trend. However, its development is being impeded because existing works cannot produce suitable sketches for data-driven research. These works either generate sketches lacking a freehand style or utilize generative models not originally designed for this task resulting in poor effectiveness. To address this issue, we design a two-stage generative framework mimicking the human sketching behavior pattern, called MSFormer, which is the first time to produce humanoid freehand sketches tailored for mechanical components. The first stage employs Open CASCADE technology to obtain multi-view contour sketches from mechanical components, filtering perturbing signals for the ensuing generation process. Meanwhile, we design a view selector to simulate viewpoint selection tasks during human sketching for picking out information-rich sketches. The second stage translates contour sketches into freehand sketches by a transformer-based generator. To retain essential modeling features as much as possible and rationalize stroke distribution, we introduce a novel edge-constraint stroke initialization. Furthermore, we utilize a CLIP vision encoder and a new loss function incorporating the Hausdorff distance to enhance the generalizability and robustness of the model. Extensive experiments demonstrate that our approach achieves state-of-the-art performance for generating freehand sketches in the mechanical domain. Project page: https://mcfreeskegen.github.io/.