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Changbo Wang

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

GT2-GS: Geometry-aware Texture Transfer for Gaussian Splatting

Wenjie Liu
Zhongliang Liu
Junwei Shu
Changbo Wang
Yang Li

Transferring 2D textures onto complex 3D scenes plays a vital role in enhancing the efficiency and controllability of 3D multimedia content creation. However, existing 3D style transfer methods primarily focus on transferring abstract artistic styles to 3D scenes. These methods often overlook the geometric information of the scene, which makes it challenging to achieve high-quality 3D texture transfer results. In this paper, we present GT2-GS, a geometry-aware texture transfer framework for gaussian splatting. First, we propose a geometry-aware texture transfer loss that enables view-consistent texture transfer by leveraging prior view-dependent feature information and texture features augmented with additional geometric parameters. Moreover, an adaptive fine-grained control module is proposed to address the degradation of scene information caused by low-granularity texture features. Finally, a geometry preservation branch is introduced. This branch refines the geometric parameters using additionally bound Gaussian color priors, thereby decoupling the optimization objectives of appearance and geometry. Extensive experiments demonstrate the effectiveness and controllability of our method. Through geometric awareness, our approach achieves texture transfer results that better align with human visual perception.

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AAAI Conference 2026 Conference Paper

MPJudge: Towards Perceptual Assessment of Music-Induced Paintings

Shiqi Jiang
Tianyi Liang
Huayuan Ye
Changbo Wang
Chenhui Li

Music-induced painting is a unique artistic practice, where visual artworks are created under the influence of music. Evaluating whether a painting faithfully reflects the music that inspired it poses a challenging perceptual assessment task. Existing methods primarily rely on emotion recognition models to assess the similarity between music and painting, but such models introduce considerable noise and overlook broader perceptual cues beyond emotion. To address these limitations, we propose a novel framework for music-induced painting assessment that directly models perceptual coherence between music and visual art. We introduce MPD, the first large-scale dataset of music–painting pairs annotated by domain experts based on perceptual coherence. To better handle ambiguous cases, we further collect pairwise preference annotations. Building on this dataset, we present MPJudge, a model that integrates music features into a visual encoder via a modulation-based fusion mechanism. To effectively learn from ambiguous cases, we adopt Direct Preference Optimization for training. Extensive experiments demonstrate that our method outperforms existing approaches. Qualitative results further show that our model more accurately identifies music-relevant regions in paintings.

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IROS Conference 2025 Conference Paper

DPSN: Dual Prior Knowledge Induced Tactile paving and Obstacle Joint Segmentation Network

Youqi Song
Wenqi Li
Zhao Zhang
Yu Wu
Zilong Jin
Changbo Wang
Gaoqi He

Accurate semantic segmentation of both tactile paving and the obstacle is crucial for the safe mobility of visually impaired individuals. However, existing methods face two major challenges: (i) discontinuous segmentation fragments; (ii) Inaccurate obstacle recognition. To address challenge (i), we propose incorporating appearance priors of complete tactile pavings to prevent the model from directly learning irregular ground truth masks. To tackle challenge (ii), we propose introducing cross-modal semantic priors to complement the semantic information of obstacles. We implemented these strategies in proposed Dual Prior knowledge induced tactile paving and obstacle joint Segmentation Network (DPSN). Based on bilateral network architecture, DPSN merges obstacle category masks into tactile paving categories, constructing a complete tactile paving mask. Utilizing the complete mask, DPSN transfer appearance prior knowledge to detail features from boundary and structural perspectives. Concurrently, DPSN leverages the CLIP Text Encoder to guide visual feature decoding by attention mechanisms, transferring rich cross-modal semantic prior knowledge to the visual feature maps. Furthermore, we propose the TPO-Dataset, the first dataset for joint tactile paving and obstacle segmentation acquired from actual scenes. Experiments demonstrate that DPSN achieves state-of-the-art results on the TPO-Dataset, with relative gains of 27. 16% in obstacle IoU and 30. 53% in accuracy metrics compared to baseline methods. Notably, DPSN achieves real-time performance at 88. 25 FPS on the maximum scale of 2048×512 resolution.

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AAAI Conference 2025 Conference Paper

Motion-Zero: A Zero-Shot Trajectory Control Framework of Moving Object for Diffusion-Based Video Generation

Changgu Chen
Junwei Shu
Gaoqi He
Changbo Wang
Yang Li

Recent large-scale pre-trained diffusion models have demonstrated a powerful generative ability to produce high-quality videos from detailed text descriptions. However, exerting control over the motion of objects in videos generated by any video diffusion model remains a challenging problem. In this paper, we propose a novel zero-shot moving object trajectory control framework, Motion-Zero, to enable arbitrary single-object-trajectory control for the text-to-video diffusion model. To this end, an initial noise prior module is designed to provide a position-based prior to improve the stability of the appearance of the moving object and the accuracy of position. In addition, based on the attention map of the U-Net, spatial constraints are directly applied to the denoising process of diffusion models, which further ensures the positional consistency of moving objects during the inference. Furthermore, temporal consistency is guaranteed with a proposed shift temporal attention mechanism. Our method can be flexibly applied to various state-of-the-art video diffusion models without any training process. Extensive experiments demonstrate our proposed method can control the motion trajectories of arbitrary objects while preserving the original ability to generate high-quality videos.

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ICML Conference 2025 Conference Paper

TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation

Tianyi Liang 0002
Jiangqi Liu
Yifei Huang 0006
Shiqi Jiang 0001
Jianshen Shi
Changbo Wang
Chenhui Li 0001

Text-to-image (T2I) generation has made remarkable progress in producing high-quality images, but a fundamental challenge remains: creating backgrounds that naturally accommodate text placement without compromising image quality. This capability is non-trivial for real-world applications like graphic design, where clear visual hierarchy between content and text is essential. Prior work has primarily focused on arranging layouts within existing static images, leaving unexplored the potential of T2I models for generating text-friendly backgrounds. We present TextCenGen, a training-free approach that actively relocates objects before optimizing text regions, rather than directly reducing cross-attention which degrades image quality. Our method introduces: (1) a force-directed graph approach that detects conflicting objects and guides them relocation using cross-attention maps, and (2) a spatial attention constraint that ensures smooth background generation in text regions. Our method is plug-and-play, requiring no additional training while well balancing both semantic fidelity and visual quality. Evaluated on our proposed text-friendly T2I benchmark of 27, 000 images across three seed datasets, TextCenGen outperforms existing methods by achieving 23% lower saliency overlap in text regions while maintaining 98% of the original semantic fidelity measured by CLIP score and our proposed Visual-Textual Concordance Metric (VTCM).

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AAAI Conference 2024 Conference Paper

AACP: Aesthetics Assessment of Children’s Paintings Based on Self-Supervised Learning

Shiqi Jiang
Ning Li
Chen Shi
Liping Guo
Changbo Wang
Chenhui Li

The Aesthetics Assessment of Children's Paintings (AACP) is an important branch of the image aesthetics assessment (IAA), playing a significant role in children's education. This task presents unique challenges, such as limited available data and the requirement for evaluation metrics from multiple perspectives. However, previous approaches have relied on training large datasets and subsequently providing an aesthetics score to the image, which is not applicable to AACP. To solve this problem, we construct an aesthetics assessment dataset of children's paintings and a model based on self-supervised learning. 1) We build a novel dataset composed of two parts: the first part contains more than 20k unlabeled images of children's paintings; the second part contains 1.2k images of children's paintings, and each image contains eight attributes labeled by multiple design experts. 2) We design a pipeline that includes a feature extraction module, perception modules and a disentangled evaluation module. 3) We conduct both qualitative and quantitative experiments to compare our model's performance with five other methods using the AACP dataset. Our experiments reveal that our method can accurately capture aesthetic features and achieve state-of-the-art performance.

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NeurIPS Conference 2024 Conference Paper

ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model

Yiming Sun
Fan Yu
Shaoxiang Chen
Yu Zhang
Junwei Huang
Yang Li
Chenhui Li
Changbo Wang

Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance. We found that this inferiority primarily results from their heavy reliance on manual textual annotations, which include the frequent provision of ambiguous language descriptions. In this paper, we propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions and enhance tracking performance. To this end, we propose a novel reflection-based prompt optimization module to iteratively refine the ambiguous and inaccurate descriptions of the target with tracking feedback. To further utilize semantic information produced by MLLM, a simple yet effective VL tracking framework is proposed and can be easily integrated as a plug-and-play module to boost the performance of both VL and visual trackers. Experimental results show that our proposed ChatTracker achieves a performance comparable to existing methods.

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AAAI Conference 2024 Conference Paper

Kumaraswamy Wavelet for Heterophilic Scene Graph Generation

Lianggangxu Chen
Youqi Song
Shaohui Lin
Changbo Wang
Gaoqi He

Graph neural networks (GNNs) has demonstrated its capabilities in the field of scene graph generation (SGG) by updating node representations from neighboring nodes. Actually it can be viewed as a form of low-pass filter in the spatial domain, which smooths node feature representation and retains commonalities among nodes. However, spatial GNNs does not work well in the case of heterophilic SGG in which fine-grained predicates are always connected to a large number of coarse-grained predicates. Blind smoothing undermines the discriminative information of the fine-grained predicates, resulting in failure to predict them accurately. To address the heterophily, our key idea is to design tailored filters by wavelet transform from the spectral domain. First, we prove rigorously that when the heterophily on the scene graph increases, the spectral energy gradually shifts towards the high-frequency part. Inspired by this observation, we subsequently propose the Kumaraswamy Wavelet Graph Neural Network (KWGNN). KWGNN leverages complementary multi-group Kumaraswamy wavelets to cover all frequency bands. Finally, KWGNN adaptively generates band-pass filters and then integrates the filtering results to better accommodate varying levels of smoothness on the graph. Comprehensive experiments on the Visual Genome and Open Images datasets show that our method achieves state-of-the-art performance.

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AAAI Conference 2024 Conference Paper

Multi-Prototype Space Learning for Commonsense-Based Scene Graph Generation

Lianggangxu Chen
Youqi Song
Yiqing Cai
Jiale Lu
Yang Li
Yuan Xie
Changbo Wang
Gaoqi He

In the domain of scene graph generation, modeling commonsense as a single-prototype representation has been typically employed to facilitate the recognition of infrequent predicates. However, a fundamental challenge lies in the large intra-class variations of the visual appearance of predicates, resulting in subclasses within a predicate class. Such a challenge typically leads to the problem of misclassifying diverse predicates due to the rough predicate space clustering. In this paper, inspired by cognitive science, we maintain multi-prototype representations for each predicate class, which can accurately find the multiple class centers of the predicate space. Technically, we propose a novel multi-prototype learning framework consisting of three main steps: prototype-predicate matching, prototype updating, and prototype space optimization. We first design a triple-level optimal transport to match each predicate feature within the same class to a specific prototype. In addition, the prototypes are updated using momentum updating to find the class centers according to the matching results. Finally, we enhance the inter-class separability of the prototype space through iterations of the inter-class separability loss and intra-class compactness loss. Extensive evaluations demonstrate that our approach significantly outperforms state-of-the-art methods on the Visual Genome dataset.

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AAAI Conference 2023 Conference Paper

Explicit Invariant Feature Induced Cross-Domain Crowd Counting

Yiqing Cai
Lianggangxu Chen
Haoyue Guan
Shaohui Lin
Changhong Lu
Changbo Wang
Gaoqi He

Cross-domain crowd counting has shown progressively improved performance. However, most methods fail to explicitly consider the transferability of different features between source and target domains. In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. The main idea is to explicitly extract domain-invariant features from both source and target domains, which builds a bridge to transfer more rich knowledge between two domains. The framework consists of three parts, global feature decoupling (GFD), relation exploration and alignment (REA), and graph-guided knowledge enhancement (GKE). In the GFD module, domain-invariant features are efficiently decoupled from domain-specific ones in two domains, which allows the model to distinguish crowds features from backgrounds in the complex scenes. In the REA module both inter-domain relation graph (Inter-RG) and intra-domain relation graph (Intra-RG) are built. Specifically, Inter-RG aggregates multi-scale domain-invariant features between two domains and further aligns local-level invariant features. Intra-RG preserves taskrelated specific information to assist the domain alignment. Furthermore, GKE strategy models the confidence of pseudolabels to further enhance the adaptability of the target domain. Various experiments show our method achieves state-of-theart performance on the standard benchmarks. Code is available at https://github.com/caiyiqing/IF-CKT.

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ICRA Conference 2019 Conference Paper

Transferring Grasp Configurations using Active Learning and Local Replanning

Hao Tian 0003
Changbo Wang
Dinesh Manocha
Xinyu Zhang 0002

We present a new approach to transfer grasp configurations from prior example objects to novel objects. We assume the novel and example objects have the same topology and similar shapes. We perform 3D segmentation on these objects using geometric and semantic shape characteristics. We compute a grasp space for each part of the example object using active learning. We build bijective contact mapping between these model parts and compute the corresponding grasps for novel objects. Finally, we assemble the individual parts and use local replanning to adjust grasp configurations while maintaining its stability and physical constraints. Our approach is general, can handle all kind of objects represented using mesh or point cloud and a variety of robotic hands.

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