Comprehending occluded objects remains a critical challenge for multi-modal large language models due to missing visual representations. Current approaches either rely on multi-stage pipelines with error propagation, or use unified encoders that fail to balance understanding and generation. We propose OCC-MLLM-V2, an end-to-end autoregressive framework with three key innovations: (1) Hierarchical Trinity Fusion Architecture integrating multi-view RGB, hand masks, and 3D reconstructions via Adaptive Weight Image Fusion and Spatial Attention Affine Fusion; (2) Visual Dual Encoder employing SigLIP for understanding and VQ tokenizers for generation; (3) Visual Dual Decoder with joint optimization. Unlike pipeline methods, our unified framework eliminates sequential dependencies and enables end-to-end gradient flow. Experiments demonstrate improvements: 6.27% gains on ObMan and 6.35% on DexYCB across 1B-8B models, while reducing FLOPs by 32%–59% and inference time by 9.5%–37%. Our framework surpasses GPT-4o and Gemini 2.5-Pro on multiple occlusion benchmarks. Code and data are available at https://github.com/chaoyiwang09/OCC-MLLM .

Fashion design is a challenging and complex process. Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice. In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff. Our model is designed to mimic the practical fashion design workflow, by unraveling the denosing process into two successive stages: 1) an ideation stage that generates design proposals given high-level concepts and 2) an iteration stage that continuously refines the proposals using low-level attributes. Our model supports fashion design generation and fine-grained local editing in a single framework. To train our model, we contribute a new dataset of full-body fashion images annotated with hierarchical text descriptions. Extensive evaluations show that, as compared to prior approaches, our method can generate fashion designs and edited results with higher fidelity and better prompt adherence, showing its promising potential to augment the practical fashion design workflow.

One fascinating aspect of pre-trained Audio-Language Models (ALMs) learning is their impressive zero-shot generalization capability and test-time adaptation (TTA) methods aiming to improve domain performance without annotations. However, previous test time adaptation (TTA) methods for ALMs in zero-shot classification tend to be stuck in incorrect model predictions. In order to further boost the performance, we propose multiple guidance on prompt learning without annotated labels. First, guidance of consistency on both context tokens and domain tokens of ALMs is set. Second, guidance of both consistency across multiple augmented views of each single test sample and contrastive learning across different test samples is set. Third, we propose a corresponding end-end learning framework for the proposed test-time adaptation method without annotated labels. We extensively evaluate our approach on 12 downstream tasks across domains, our proposed adaptation method leads to 4.41% (max 7.50%) average zero-shot performance improvement in comparison with the state-of-the-art models.

While recent video-to-audio (V2A) models can generate realistic background audio from visual input, they largely overlook speech, an essential part of many video soundtracks. This paper proposes a new task, video-to-soundtrack (V2ST) generation, which aims to jointly produce synchronized background audio and speech within a unified framework. To tackle V2ST, we introduce DualDub, a unified framework built on a multimodal language model that integrates a multimodal encoder, a cross-modal aligner, and dual decoding heads for simultaneous background audio and speech generation. Specifically, our proposed cross-modal aligner employs causal and non-causal attention mechanisms to improve synchronization and acoustic harmony. Besides, to handle data scarcity, we design a curriculum learning strategy that progressively builds the multimodal capability. Finally, we introduce DualBench, the first benchmark for V2ST evaluation with a carefully curated test set and comprehensive metrics. Experimental results demonstrate that DualDub achieves state-of-the-art performance, generating high-quality and well-synchronized soundtracks with both speech and background audio. DualBench and generated samples of DualDub are available at https://github.com/wjtian-wonderful/DualBench.

The study of reconstruction of hands and objects from color monocular images has garnered considerable attention in recent years. In existing methods, parametric models are constructed at single scale, and the interaction between hands and objects has not fully be explored. As a result, the multiscale information in 2D images cannot be fully exploited. At the same time, the lack of feature fusion and insufficient utilization of labels also have a great impact on the reconstruction accuracy. To address the limitations, a new framework is proposed, which comprises three key modules. Firstly, a multiscale feature extractor, which generates a multiscale representation of feature, is used to capture the interaction between hand and object more effectively. Secondly, a bridge based on attention has been used to establish the connection between hand and object representations, which facilitates the integration of them. Lastly, a module based on token merge is introduced into the framework, which provides the segmentation representation of object. The experimental results on two datasets, named Obman and DexYCB, demonstrated that the proposed method had good performance and achieved a shape error about 0.121$\text{cm}^{2}$on Obman and 0.40$\text{cm}^{2}$on DexYCB, outperforming the state-of-the-art methods. This study will probably provide the human-computer interaction methods with broader application prospects.

A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and ITERATION scheme to learn the representation of nodes. However, information between nodes is inevitably lost in the scheme during learning. In order to reduce the loss, we extend the GNNs frameworks by exploring the aggregation and iteration scheme in the methodology of mutual information. We propose a new approach of enlarging the normal neighborhood in the aggregation of GNNs, which aims at maximizing mutual information. Based on a series of experiments conducted on several benchmark datasets, we show that the proposed approach improves the state-of-the-art performance for four types of graph tasks, including supervised and semi-supervised graph classification, graph link prediction and graph edge generation and classification.

We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.