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Weiwei Xu

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

FashionMAC: Deformation-Free Fashion Image Generation with Fine-Grained Model Appearance Customization

Rong Zhang
Jinxiao Li
Jingnan Wang
Zhiwen Zuo
Jianfeng Dong
Wei Li
Chi Wang
Weiwei Xu

Garment-centric fashion image generation aims to synthesize realistic and controllable human models dressing a given garment, which has attracted growing interest due to its practical applications in e-commerce. The key challenges of the task lie in two aspects: (1) faithfully preserving the garment details, and (2) gaining fine-grained controllability over the model's appearance. Existing methods typically require performing garment deformation in the generation process, which often leads to garment texture distortions. Also, they fail to control the fine-grained attributes of the generated models, due to the lack of specifically designed mechanisms. To address these issues, we propose FashionMAC, a novel diffusion-based deformation-free framework that achieves high-quality and controllable fashion showcase image generation. The core idea of our framework is to eliminate the need for performing garment deformation and directly outpaint the garment segmented from a dressed person, which enables faithful preservation of the intricate garment details. Moreover, we propose a novel region-adaptive decoupled attention (RADA) mechanism along with a chained mask injection strategy to achieve fine-grained appearance controllability over the synthesized human models. Specifically, RADA adaptively predicts the generated regions for each fine-grained text attribute and enforces the text attribute to focus on the predicted regions by a chained mask injection strategy, significantly enhancing the visual fidelity and the controllability. Extensive experiments validate the superior performance of our framework compared to existing state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Mimic-X: A Large-Scale Motion Dataset via Fast Physics-Based Controller Adaptation

Hongyu Tao
Shuaiying Hou
Junheng Fang
Mingyao Shi
Weiwei Xu

Large and high-quality motion datasets are essential for advancing human motion modeling. However, limitations of existing motion datasets, such as insufficient scale or inadequate quality, significantly hinder the progress of this field. To address these limitations, we introduce Mimic-X, a large-scale (52 hours), physically plausible 3D human motion dataset. To construct Mimic-X, we develop an adaptive option framework that controls a physically simulated character to imitate low-quality motions extracted from a vast collection of online videos. Specifically, we first apply hierarchical clustering to group motions into clusters, and then train option policies to mimic motions sampled from these clusters. Considering the noisy nature of low-quality motions, we utilize a separate encoder for each cluster to map the noisy motions within the cluster into a compact latent space. This significantly enhances the quality of the imitated motions while accelerating the learning process. Subsequently, we employ dynamic programming as a meta-policy to efficiently organize the option policies to generate complete motion clips. Finally, we perform fine-tuning to each motion sequence to further refine motion quality. The proposed adaptive option framework outperforms state-of-the-art human motion recovery methods across various evaluation metrics, demonstrating that motions in Mimic-X exhibit higher quality and greater physical plausibility. Furthermore, experimental results show that Mimic-X enhances the performance of motion generation methods, verifying its effectiveness for motion modeling tasks.

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EAAI Journal 2025 Journal Article

Fast prediction and compensation of curing deformation behaviours of composite parts with complex geometry based on neural operator on Riemannian manifolds

Lu Chen
Yingguang Li
Jingyan Su
Weiwei Xu
Lin Hu
Gengxiang Chen
Xu Liu
Xiaozhong Hao

Controlling the curing deformation of composite parts is becoming increasingly challenging with the ever-increasing performance requirements of aerospace equipment. Mould surface compensation, which adjusts the mould surface to minimise the discrepancy between the cured part geometry and the nominal part geometry, has become a primary deformation control way in engineering. The existing mirror compensation methods focus on the deformation dominated by spring-in and are challenging for complex deformation modes. Surrogate model-based shape optimisation provides a feasible idea, but establishing a surrogate model to predict curing deformation fields on complex part geometries remains a challenge. Therefore, this study explores a novel neural operator-driven framework for fast curing deformation prediction and compensation. A clustering-based deformation field segmentation method is proposed to manipulate the mould surface morphing using limited design variables. The neural operator on Riemannian manifolds is introduced for the first time to establish the surrogate model between the mould surface and the curing deformation fields on complex part geometries. To control the global error distribution of the composite part, two error metrics are designed to optimise the mould surface by genetic algorithm. The verification results show that the proposed framework exhibits significant potential in predicting and compensating for the curing deformation field of composite parts with complex geometry.

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AAAI Conference 2025 Conference Paper

OmniSR: Shadow Removal Under Direct and Indirect Lighting

Jiamin Xu
Zelong Li
Yuxin Zheng
Chenyu Huang
Renshu Gu
Weiwei Xu
Gang Xu

Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms. The experiments show that our method outperforms state-of-the-art shadow removal techniques and can effectively generalize to indoor and outdoor scenes under various lighting conditions, enhancing the overall effectiveness and applicability of shadow removal methods.

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IROS Conference 2025 Conference Paper

Real-Time Consistent Monocular Depth Recovery System for Dynamic Environments

Gan Huang
Xiaokun Pan
Hengxu Lin
Ziyang Zhang
Weiwei Xu
Guofeng Zhang 0001

Monocular depth estimation is essential for applications such as autonomous navigation and 3D reconstruction. However, achieving accurate and temporally consistent depth estimation in dynamic environments remains challenging due to scale ambiguity, sensitivity to dynamic objects, and inconsistent depth predictions. Traditional SLAM-based methods ensure global consistency but perform poorly in dynamic scenes, while deep learning-based approaches suffer from the absence of absolute scale and temporal stability. To address these issues, we propose a Real-Time Consistent Monocular Depth Recovery System that combines ORB-SLAM3 for sparse depth initialization, a ViT-based depth completion network, and a motion segmentation module to improve robustness in dynamic environments. Additionally, we introduce a dual-weight fusion module that adaptively balances RGB semantic features and geometric depth priors, ensuring high accuracy and consistency. Our system jointly optimizes both static and dynamic regions to produce globally scale-consistent dense depth maps with improved temporal stability. Extensive experiments on benchmark datasets demonstrate that our approach outperforms existing methods in terms of depth accuracy, temporal consistency, and robustness in dynamic scenes, while maintaining real-time performance.

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NeurIPS Conference 2025 Conference Paper

UniTransfer: Video Concept Transfer via Progressive Spatio-Temporal Decomposition

guojun lei
Rong Zhang
Tianhang Liu
Hong Li
Zhiyuan Ma
Chi Wang
Weiwei Xu

Recent advancements in video generation models have enabled the creation of diverse and realistic videos, with promising applications in advertising and film production. However, as one of the essential tasks of video generation models, video concept transfer remains significantly challenging. Existing methods generally model video as an entirety, leading to limited flexibility and precision when solely editing specific regions or concepts. To mitigate this dilemma, we propose a novel architecture UniTransfer, which introduces both spatial and diffusion timestep decomposition in a progressive paradigm, achieving precise and controllable video concept transfer. Specifically, in terms of spatial decomposition, we decouple videos into three key components: the foreground subject, the background, and the motion flow. Building upon this decomposed formulation, we further introduce a dual-to-single-stream DiT-based architecture for supporting fine-grained control over different components in the videos. We also introduce a self-supervised pretraining strategy based on random masking to enhance the decomposed representation learning from large-scale unlabeled video data. Inspired by the Chain-of-Thought reasoning paradigm, we further revisit the denoising diffusion process and propose a Chain-of-Prompt (CoP) mechanism to achieve the timestep decomposition. We decompose the denoising process into three stages of different granularity and leverage large language models (LLMs) for stage-specific instructions to guide the generation progressively. We also curate an animal-centric video dataset called OpenAnimal to facilitate the advancement and benchmarking of research in video concept transfer. Extensive experiments demonstrate that our method achieves high-quality and controllable video concept transfer across diverse reference images and scenes, surpassing existing baselines in both visual fidelity and editability.

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AAAI Conference 2022 Conference Paper

Active Boundary Loss for Semantic Segmentation

Chi Wang
Yunke Zhang
Miaomiao Cui
Peiran Ren
Yin Yang
Xuansong Xie
Xian-Sheng Hua
Hujun Bao

This paper proposes a novel active boundary loss for semantic segmentation. It can progressively encourage the alignment between predicted boundaries and ground-truth boundaries during end-to-end training, which is not explicitly enforced in commonly used cross-entropy loss. Based on the predicted boundaries detected from the segmentation results using current network parameters, we formulate the boundary alignment problem as a differentiable direction vector prediction problem to guide the movement of predicted boundaries in each iteration. Our loss is model-agnostic and can be plugged in to the training of segmentation networks to improve the boundary details. Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union on challenging image and video object segmentation datasets.

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IJCAI Conference 2022 Conference Paper

Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs

Min Zhou
Chenchen Xu
Ye Ma
Tiezheng Ge
Yuning Jiang
Weiwei Xu

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking design elements (e. g. , texts and embellishments) as model inputs, which inevitably leaves hint of the ground truth. We study the misalignment between the training inputs (with hint masks) and test inputs (without masks), and design a novel domain alignment module (DAM) to narrow this gap. For training, we built a large-scale layout dataset which consists of 60, 548 advertising posters with annotated layout information. To evaluate the generated layouts, we propose three novel metrics according to aesthetic intuitions. Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions. The data and code will be available at https: //github. com/minzhouGithub/CGL-GAN.

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NeurIPS Conference 2022 Conference Paper

Geometry-aware Two-scale PIFu Representation for Human Reconstruction

Zheng Dong
Ke Xu
Ziheng Duan
Hujun Bao
Weiwei Xu
Rynson Lau

Although PIFu-based 3D human reconstruction methods are popular, the quality of recovered details is still unsatisfactory. In a sparse (e. g. , 3 RGBD sensors) capture setting, the depth noise is typically amplified in the PIFu representation, resulting in flat facial surfaces and geometry-fallible bodies. In this paper, we propose a novel geometry-aware two-scale PIFu for 3D human reconstruction from sparse, noisy inputs. Our key idea is to exploit the complementary properties of depth denoising and 3D reconstruction, for learning a two-scale PIFu representation to reconstruct high-frequency facial details and consistent bodies separately. To this end, we first formulate depth denoising and 3D reconstruction as a multi-task learning problem. The depth denoising process enriches the local geometry information of the reconstruction features, while the reconstruction process enhances depth denoising with global topology information. We then propose to learn the two-scale PIFu representation using two MLPs based on the denoised depth and geometry-aware features. Extensive experiments demonstrate the effectiveness of our approach in reconstructing facial details and bodies of different poses and its superiority over state-of-the-art methods.

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AAAI Conference 2020 Conference Paper

AutoRemover: Automatic Object Removal for Autonomous Driving Videos

Rong Zhang
Wei Li
Peng Wang
Chenye Guan
Jin Fang
Yuhang Song
Jinhui Yu
Baoquan Chen

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm AutoRemover, designed speciﬁcally for generating street-view videos without any moving objects. In our setup we have two challenges: the ﬁrst is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More speciﬁcally, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over 19%.

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IROS Conference 2008 Conference Paper

Shape-grinding by direct position / force control with on-line constraint estimation

Mamoru Minami
Weiwei Xu

Based on the analysis of the interaction between a manipulatorpsilas hand and a working object, a model representing the constrained dynamics of the robot is first discussed. The constrained forces are expressed by an algebraic function of states, input generalized forces, and constraint condition, and then direct position / force controller without force sensor is proposed based on the algebraic relation. To give the grinding system the ability to adapt to any object shape being changed by the grinding, we added estimating function of the constraint condition in real time for the adaptive position / force control. Evaluations through simulations by fitting the changing constraint surface with spline functions, indicate that reliable position / force control and shape-grinding can be achieved by the proposed controller.

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