This article investigates the leader-follower formation control of flight vehicles subject to speed and control acceleration constraints. The objective of the flight vehicles is to track a virtual leader in a nominal configuration, while the speeds and control accelerations of the flight vehicles are restricted within certain ranges. A distributed extended state observer (DESO) featuring practical predefined-time convergence is proposed for the followers to estimate the leader's position and velocity. Then, an adaptive finite-time position tracking control law is developed so that the followers form the expected formation by tracking the expected positions related to the estimation of the virtual leader's information and the nominal configuration. The speed constraint is satisfied by leveraging a transformation based on the inverse hyperbolic tangent function, while an adaptive scheme exploiting the integral barrier Lyapunov function (IBLF) is proposed to address the control acceleration constraints. Numerical simulations are conducted to validate the proposed method.

Text-guided 3D face synthesis has achieved remarkable results by leveraging text-to-image (T2I) diffusion models. However, most existing works focus solely on the direct gen-eration, ignoring the editing, restricting them from synthe-sizing customized 3D faces through iterative adjustments. In this paper, we propose a unified text-guided framework from face generation to editing. In the generation stage, we propose a geometry-texture decoupled generation to miti-gate the loss of geometric details caused by coupling. Be-sides, decoupling enables us to utilize the generated geom-etry as a condition for texture generation, yielding highly geometry-texture aligned results. We further employ a fine-tuned texture diffusion model to enhance texture quality in both RGB and YUV space. In the editing stage, we first em-ploy a pre-trained diffusion model to update facial geometry or texture based on the texts. To enable sequential editing, we introduce a UV domain consistency preservation reg-ularization, preventing unintentional changes to irrelevant facial attributes. Besides, we propose a self-guided consis-tency weight strategy to improve editing efficacy while pre-serving consistency. Through comprehensive experiments, we showcase our method's superiority in face synthesis. Project page: https://faceg2e.github.io/.

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently. First, we design a multidomain consistent representation (MDCR) to eliminate semantic gaps by capturing intermodal features while maintaining lossless intramodal semantics in multidomains. Furthermore, we present neighbor-based relation inpainting (NRI) to reduce semantic ambiguity via repairing fuzzy relations with correlations of relations. Finally, we propose hierarchical multisemantics aggregation (HMSA) to guarantee the completeness of semantics by aggregating features of nodes and relations with a reconstruction network (RN). Experimental results show that IMR-HGN could extract the intermodal semantics of answers, achieving a 2.16% improvement on the validation set of the TQA dataset and a 3.04% increase on the test set of the AI2D dataset.

This article investigates the problem of cooperative guidance against maneuvering target under malicious attacks. In consideration of the false-data injection attacks (FDIAs), a reputation-based cooperative guidance law with fault tolerance is proposed to drive multiflight vehicles to reach a maneuvering target simultaneously. A novel prescribed performance function (PPF) with predefined-time convergence is presented by taking into account the limitation of available capacity. By incorporating the reputation system based on confidence factors and trust factors, which are leveraged to identify the attacked communication links or vehicle members, the fault-tolerant behavior can be achieved to resist the effects resulted from the FDIAs. The effectiveness of the reputation-based fault-tolerant cooperative guidance method is verified by numerical simulation.

The data-hungry nature of deep learning and the high cost of annotating point-level labels for point clouds make it difficult to apply semantic segmentation methods to unlabeled real-world indoor scenes. Therefore, label-efficient point cloud segmentation has become a promising research topic. We noticed that the online housing design platforms can provide a large number of synthetic indoor 3D scenes, which are created with semantic labels. In this paper, we propose to learn semantic segmentation on synthetic point clouds and adapt the model for unlabeled real-world data. The main challenge is that directly using models trained on synthetic data for real-world data produces poor results due to the large domain gap between synthetic and real-world data. We design a point cloud style transfer network and a feature discrimination network to reduce the domain gap in both the input space and the feature space. Experiments show that our approach significantly improves the performance on real-world data for models learned from synthetic data.

Query graph as a junction of semantic parsing in question answering over knowledge bases (KBQA) connects questions and logical queries. Though query graph consists of rich information such as structure, relation, etc., the current KBQA's models mainly utilize limited relation information in a naive way. It is not easy to learn the representation of a query graph with that information due to the heterogeneity of the query graph and intricate correlation of relations. In this paper, we propose a Global Semantic-based Message Passing (GSMP) model to model the global semantics of a query graph from its structure and relation information. In GSMP, we present a recurrent-based relational graph convolutional network (RGCN) to capture heterogeneous query graphs where the recurrent unit improves the capability of RGCN in processing small-scale query graphs. Moreover, we present a contextual-based method to remove ambiguity caused by intricate correlations where the contextual adjacency of relations optimizes relation representation. Finally, we present a nonlinear gate-based encoder to learning the representation of questions' syntactic tree, as the structure information of questions, for better matching the global semantics of query graphs. Experiments evaluated on benchmarks show that our model outperforms off-the-shelf models.

3D object reconstruction from a single image is a highly ill-posed problem, requiring strong prior knowledge of 3D shapes. Deep learning methods are popular for this task. Especially, most works utilized 3D deconvolution to generate 3D shapes. However, the resolution of results is limited by the high resource consumption of 3D deconvolution. In this paper, we propose SliceNet, sequentially generating 2D slices of 3D shapes with shared 2D deconvolution parameters. To capture relations between slices, the RNN is also introduced. Our model has three main advantages: First, the introduction of RNN allows the CNN to focus more on local geometry details,improving the results’ fine-grained plausibility. Second, replacing 3D deconvolution with 2D deconvolution reducs much consumption of memory, enabling higher resolution of final results. Third, an slice-aware attention mechanism is designed to provide dynamic information for each slice’s generation, which helps modeling the difference between multiple slices, making the learning process easier. Experiments on both synthesized data and real data illustrate the effectiveness of our method.

Abstract Learning‐based 3D generation is a popular research field in computer graphics. Recently, some works adapted implicit function defined by a neural network to represent 3D objects and have become the current state‐of‐the‐art. However, training the network requires precise ground truth 3D data and heavy pre‐processing, which is unrealistic. To tackle this problem, we propose the DFR, a differentiable process for rendering implicit function representation of 3D objects into 2D images. Briefly, our method is to simulate the physical imaging process by casting multiple rays through the image plane to the function space, aggregating all information along with each ray, and performing a differentiable shading according to every ray's state. Some strategies are also proposed to optimize the rendering pipeline, making it efficient both in time and memory to support training a network. With DFR, we can perform many 3D modeling tasks with only 2D supervision. We conduct several experiments for various applications. The quantitative and qualitative evaluations both demonstrate the effectiveness of our method.

Point cloud segmentation is a key problem of 3D multimedia signal processing. Existing methods usually use a single network structure which is trained by a per-point loss. These methods mainly focus on the geometric similarity between the prediction results and the ground truth, ignoring visual perception difference. In this paper, we present a segmentation adversarial network to overcome the drawbacks above. A discriminator is introduced to provide a perceptual loss to increase the rationality judgment of prediction and guide the further optimization of the segmentator. In order to perfectly capture the structural information of parts in the same category of objects, condition settings are employed to add a global constraint. Experimental results show the proposed methods can correct the common errors in point cloud segmentation and obtain more accurate and better segmentation of visual perceptual.

Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for real-world co-segmentation. This paper proposes a novel end-to-end deep learning approach for group-wise object co-segmentation with a recurrent network architecture. Specifically, the semantic features extracted from a pre-trained CNN of each image are first processed by single image representation branch to learn the unique properties. Meanwhile, a specially designed Co-Attention Recurrent Unit (CARU) recurrently explores all images to generate the final group representation by using the co-attention between images, and simultaneously suppresses noisy information. The group feature which contains synergetic information is broadcasted to each individual image and fused with multi-scale fine-resolution features to facilitate the inferring of co-segmentation. Moreover, we propose a groupwise training objective to utilize the co-object similarity and figure-ground distinctness as the additional supervision. The whole modules are collaboratively optimized in an end-to-end manner, further improving the robustness of the approach. Comprehensive experiments on three benchmarks can demonstrate the superiority of our approach in comparison with the state-of-the-art methods.