论文检索

Boyao Zhou, Shunyuan Zheng, Zhanfeng Liao, Zihan Ma, Hanzhang Tu, Boning Liu, Yebin Liu

We present Splat-SAP, a feed-forward approach to render novel views of human-centered scenes from binocular cameras with large sparsity. Gaussian Splatting has shown its promising potential in rendering tasks, but it typically necessitates per-scene optimization with dense input views. Although some recent approaches achieve feed-forward Gaussian Splatting rendering through geometry priors obtained by multi-view stereo, such approaches still require largely overlapped input views to establish the geometry prior. To bridge this gap, we leverage pixel-wise point map reconstruction to represent geometry which is robust to large sparsity for its independent view modeling. In general, we propose a two-stage learning strategy. In stage 1, we transform the point map into real space via an iterative affinity learning process, which facilitates camera control in the following. In stage 2, we project point maps of two input views onto the target view plane and refine such geometry via stereo matching. Furthermore, we anchor Gaussian primitives on this refined plane in order to render high-quality images. As a metric representation, the scale-aware point map in stage 1 is trained in a self-supervised manner without 3D supervision and stage 2 is supervised with photo-metric loss. We collect multi-view human-centered data and demonstrate that our method improves both the stability of point map reconstruction and the visual quality of free-viewpoint rendering.

Yuxiang Zhong, Jun Wei, Chaoqi Chen, Senyou An, Hui Huang

3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.

Yuan Zhong, Jingxiang Sun, Zhongbin Zhang, Liang An, Yebin Liu

Laboratory mice, particularly the C57BL/6 strain, are essential animal models in biomedical research. However, accurate 3D surface motion reconstruction of mice remains a significant challenge due to their complex non-rigid deformations, textureless fur-covered surfaces, and the lack of realistic 3D mesh models. Moreover, existing visual datasets for mice reconstruction only contain sparse viewpoints without 3D geometries. To fill the gap, we introduce MoReMouse, the first monocular dense 3D reconstruction network specifically designed for C57BL/6 mice. To achieve high-fidelity 3D reconstructions, we present three key innovations. First, we create the first high-fidelity, dense-view synthetic dataset for C57BL/6 mice by rendering a realistic, anatomically accurate Gaussian mouse avatar. Second, MoReMouse leverages a transformer-based feedforward architecture combined with triplane representation, enabling high-quality 3D surface generation from a single image, optimized for the intricacies of small animal morphology. Third, we propose geodesic-based continuous correspondence embeddings on the mouse surface, which serve as strong semantic priors, improving surface consistency and reconstruction stability, especially in highly dynamic regions like limbs and tail. Through extensive quantitative and qualitative evaluations, we demonstrate that MoReMouse significantly outperforms existing open-source methods in both accuracy and robustness.

Yingji Zhong, Kaichen Zhou, Zhihao Li, Lanqing Hong, Zhenguo Li, Dan Xu

Neural Radiance Fields (NeRF) have shown remarkable capabilities for photorealistic novel view synthesis. One major deficiency of NeRF is that dense inputs are typically required, and the rendering quality will drop drastically given sparse inputs. In this paper, we highlight the effectiveness of rendered semantics from dense novel views, and show that rendered semantics can be treated as a more robust form of augmented data than rendered RGB. Our method enhances NeRF’s performance by incorporating guidance derived from the rendered semantics. The rendered semantic guidance encompasses two levels: the supervision level and the feature level. The supervision-level guidance incorporates a bi-directional verification module that decides the validity of each rendered semantic label, while the feature-level guidance integrates a learnable codebook that encodes semantic-aware information, which is queried by each point via the attention mechanism to obtain semanticrelevant predictions. The overall semantic guidance is embedded into a self-improved pipeline.We also introduce a more challenging sparse-input indoor benchmark, where the number of inputs is limited to as few as 6. Experiments demonstrate the effectiveness of our method and it exhibits superior performance compared to existing approaches.

Mingchen Zhong, Xin Lu, Dong Li, Senyan Xu, Ruixuan Jiang, Xueyang Fu, Baocai Yin

Low-light video deblurring poses significant challenges in applications like nighttime surveillance and autonomous driving due to dim lighting and long exposures. While event cameras offer potential solutions with superior low-light sensitivity and high temporal resolution, existing fusion methods typically employ staged strategies, limiting their effectiveness against combined low-light and motion blur degradations. To overcome this, we propose CompEvent, a complex neural network framework enabling holistic full-process fusion of event data and RGB frames for enhanced joint restoration. CompEvent features two core components: 1) Complex Temporal Alignment GRU, which utilizes complex-valued convolutions and processes video and event streams iteratively via GRU to achieve temporal alignment and continuous fusion; and 2) Complex Space-Frequency Learning module, which performs unified complex-valued signal processing in both spatial and frequency domains, facilitating deep fusion through spatial structures and system-level characteristics. By leveraging the holistic representation capability of complex-valued neural networks, CompEvent achieves full-process spatiotemporal fusion, maximizes complementary learning between modalities, and significantly strengthens low-light video deblurring capability. Extensive experiments demonstrate that CompEvent outperforms SOTA methods in addressing this challenging task.

Jia-Xing Zhong, Shijie Zhao, Junlin Li, Li Zhang

Video-to-video human motion editing aims to transfer motion from a driving video to a reference video while preserving the background dynamics and the protagonist's original appearance. We identify critical limitations in existing methods that fail to capture the full complexity of human motions, particularly regarding: 1) location changes, 2) orientation variations, and 3) complicated non-upright poses. To address these challenges, we propose a framework that collaboratively "copies and pastes" 2D and 3D features across spatio-temporal dimensions into a shared representation space for motion guidance. Our approach achieves this through: 1) a mutual distillation mechanism that enhances the robustness and capability of individual encoders, and 2) a selective fusion module that adaptively weights and combines complementary information from spatio-temporal representations. To evaluate motion editing algorithms under challenging scenarios, we introduce a comprehensive benchmark dataset comprising real-world video clips from artistic gymnastics and figure skating competitions. These sports disciplines naturally encompass the three aforementioned aspects of motion complexity. Extensive experiments demonstrate that our approach significantly outperforms existing methods, particularly in handling intricate human motions.

Haoyi Zhong, Fang-Lue Zhang, Andrew Chalmers, Taehyun Rhee

While instruction-based image editing is emerging, extending it to 360° panorama introduces additional challenges. Existing methods often produce implausible results in both equirectangular projections (ERP) and perspective views. To address these limitations, we propose SE360, a novel framework for multi-condition guided object editing in 360° panoramas. At its core is a novel coarse-to-fine autonomous data generation pipeline without manual intervention. This pipeline leverages a Vision-Language Model (VLM) and adaptive projection adjustment for hierarchical analysis, ensuring the holistic segmentation of objects and their physical context. The resulting data pairs are both semantically meaningful and geometrically consistent, even when sourced from unlabeled panoramas. Furthermore, we introduce a cost-effective, two-stage data refinement strategy to improve data realism and mitigate model overfitting to erasing artifacts. Based on the constructed dataset, we train a Transformer-based diffusion model to allow flexible object editing guided by text, mask, or reference image in 360° panoramas. Our experiments demonstrate that our method outperforms existing methods in both visual quality and semantic accuracy.

Chunlin Zhong, Qiuxia Hou, Zhangjun Zhou, Yanhao Zhang, Shuang Hao, Haonan Lu, He Tang, Xiang Bai

Video captioning aims to generate comprehensive and coherent descriptions of the video content, contributing to the advancement of both video understanding and generation. However, existing methods often suffer from motion-detail imbalance, as models tend to overemphasize one aspect while neglecting the other. This imbalance results in incomplete captions, which in turn leads to a lack of consistency in video understanding and generation. To address this issue, we propose solutions from two aspects: 1) Data aspect: We constructed the Harmonizing Motion-Detail 270K (HMD-270K) dataset through a two-stage pipeline: Motion-Detail Fusion (MDF) and Fine-Grained Examination (FGE). 2) Optimization aspect: We introduce the Caption Set Equivalence Reward (CSER) based on Group Relative Policy Optimization (GRPO). CSER enhances completeness and accuracy in capturing both motion and details through unit-to-set matching and bidirectional validation. Based on the HMD-270K supervised fine-tuning and GRPO post-training with CSER, we developed OwlCap, a powerful video captioning Multi-modal Large Language Model (MLLM) with motion-detail balance. Experimental results demonstrate that OwlCap achieves significant improvements compared to baseline models on two benchmarks: the detail-focused VDC (+4.2 Acc) and the motion-focused DREAM-1K (+4.6 F1).

Zhiwen Zheng, Yiwei Ouyang, Zhao Huang, Tao Zhang, Xiaoshuai Zhang, Huiyu Zhou, Wenwen Tang, Shaowei Jiang, Jin Liu, Xingru Huang

Accurately localizing and segmenting obscured objects from faint light patterns beyond the field of view is highly challenging due to multiple scattering and medium-induced perturbations. Most existing methods, based on real-valued modeling or local convolutional operations, are inadequate for capturing the underlying physics of coherent light propagation. Moreover, under low signal-to-noise conditions, these methods often converge to non-physical solutions, severely compromising the stability and reliability of the observation. To address these challenges, we propose a novel physics-driven Wavefront Propagating Compensation Network (WavePCNet) to simulate wavefront propagation and enhance the perception of obscured objects. This WavePCNet integrates the Tri-Phase Wavefront Complex-Propagation Reprojection (TriWCP) to incorporate complex amplitude transfer operators to precisely constrain coherent propagation behavior, along with a momentum memory mechanism to effectively suppress the accumulation of perturbations. Additionally, a High-frequency Cross-layer Compensation Enhancement is introduced to construct frequency-selective pathways with multi-scale receptive fields and dynamically models structural consistency across layers, further boosting the model’s robustness and interpretability under complex environmental conditions. Extensive experiments conducted on four physically collected datasets demonstrate that WavePCNet consistently outperforms state-of-the-art methods across both accuracy and robustness.

Yushuo Zheng, Jiangyong Ying, Huiyu Duan, Chunyi Li, Zicheng Zhang, Jing Liu, Xiaohong Liu, Guangtao Zhai

Large multimodal models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks, however their knowledge and abilities in the cross-view geo-localization and pose estimation domains remain unexplored, despite potential benefits for navigation, autonomous driving, outdoor robotics, etc. To bridge this gap, we introduce GeoX-Bench, a comprehensive Benchmark designed to explore and evaluate the capabilities of LMMs in cross-view Geo-localization and pose estimation. Specifically, GeoX-Bench contains 10,859 panoramic-satellite image pairs spanning 128 cities in 49 countries, along with corresponding 755,976 question-answering (QA) pairs. Among these, 42,900 QA pairs are designated for benchmarking, while the remaining are intended to enhance the capabilities of LMMs. Based on GeoX-Bench, we evaluate the capabilities of 25 state-of-the-art LMMs on cross-view geo-localization and pose estimation tasks, and further explore the empowered capabilities of instruction-tuning. Our benchmark demonstrate that while current LMMs achieve impressive performance in geo-localization tasks, their effectiveness declines significantly on the more complex pose estimation tasks, highlighting a critical area for future improvement, and instruction-tuning LMMs on the training data of GeoX-Bench can significantly improve the cross-view geo-sense abilities.

Yan Zheng, Zhenxiao Liang, Xiaoyan Cong, Yi Yang, Lanqing Guo, Yuehao Wang, Peihao Wang, Zhangyang Wang

We explore the oscillatory behavior observed in inversion methods applied to large-scale flow models, including text-to-image and text-to-video. By employing an augmented fixed-point-inspired iterative approach to invert real-world images, we observe that the solution does not achieve convergence, instead oscillating between distinct clusters. Through both experiments on synthetic data, text-to-image and text-to-video, we demonstrate that these oscillating clusters exhibit notable semantic coherence. We offer theoretical insights, showing that this behavior arises from oscillatory dynamics in flow models. Building on this understanding, we introduce a simple and fast distribution transfer technique that facilitates training-free image and video editing/enhancement. Furthermore, we provide quantitative results demonstrating the effectiveness of our method on tasks such as image enhancement, editing, and reconstruction. Notably, our approach enables the transformation of image-only enhancers and editors into lightweight, video-capable tools—without additional training—highlighting its practical versatility and impact.

Wenli Zheng, Huiyuan Fu, Zekai Xu, Xin Wang, Huadong Ma

High-scale image super-resolution (SR) has become increasingly important with the rapid growth of mobile devices and high-resolution displays. However, current SR methods primarily focus on lower scales and generalize poorly to high-scale scenarios due to severe information loss and complex real-world degradations. In this paper, we propose a novel Selective Diffusion Distillation (SDD) framework for real-world high-scale SR, which distills reliable knowledge from a low-scale diffusion teacher to a high-scale student. Specifically, considering severe information loss in high-scale inputs, directly distilling from low-scale models may result in feature misalignment. To address this, we introduce a Degradation-aware Metric Learning (DML) approach to align feature distributions across different degradation levels. In addition, since the diffusion-based teacher may hallucinate artifacts in ambiguous regions, blindly imitating these unreliable outputs can degrade the student’s fidelity. To tackle this, we propose a Region-aware Selective Distillation (RSD) strategy to filter out uncertain predictions and adaptively supervise only on reliable areas. To evaluate the effectiveness of our method, we introduce Real-UltraSR, a new real-world benchmark that contains diverse high-scale LR-HR pairs, including x8, x10, x12, and x14. Extensive experiments demonstrate that our SDD framework achieves state-of-the-art performance across multiple benchmarks.

Tingting Zheng, Hongxun Yao, Sicheng Zhao, Yi Xiao

Recent advances in multi-instance learning (MIL) have demonstrated impressive performance in whole slide image (WSI) analysis. However, current methods search for cues and draw conclusions from all instances or regions, resulting in excessive redundant computation and suboptimal representation quality due to irrelevant and uninformative feature interference. To address these issues, we propose CICS, an efficient and general framework that performs compact information compression and selection for high-efficiency WSI analysis. In particular, CICS features two key components: (1) context-aware compression (CAC), which partitions the instance space into sub-regions and applies learnable compression to discard irrelevant components, reduce computational complexity while facilitating information selection, and (2) global-proximity selective attention (GPSA), which cherry-picks the most informative representation with a proximity-assisted global dynamic selection strategy. Building upon these innovations, CICS forms a plug-and-play module that reduces computational complexity through compact instance representations while improving feature quality by preserving the most informative cues. Extensive experiments on six WSI classification and survival prediction datasets show that CICS consistently improves the performance of multiple representative MIL methods. It achieves 2.5%, 7.7%, and 3.9% accuracy gain over the state-of-the-art Transformer-based TransMIL, Mamba-based MambaMIL, and graph-based WIKG methods on the ESCA dataset.

Shikang Zheng, Liang Feng, Xinyu Wang, Qinming Zhou, Peiliang Cai, Chang Zou, Jiacheng Liu, Yuqi Lin, Junjie Chen, Yue Ma 等

Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To reduce their substantial computational costs, feature caching techniques have been proposed to accelerate inference by reusing hidden representations from previous timesteps. However, current methods often struggle to maintain generation quality at high acceleration ratios, where prediction errors increase sharply due to the inherent instability of long-step forecasting. In this work, we adopt an ordinary differential equation (ODE) perspective on the hidden-feature sequence, modeling layer representations along the trajectory as a feature-ODE. We attribute the degradation of existing caching strategies to their inability to robustly integrate historical features under large skipping intervals. To address this, we propose FoCa (Forecast-then-Calibrate), which treats feature caching as a feature-ODE solving problem. Extensive experiments on image, video generation, and super-resolution tasks demonstrate the effectiveness of FoCa, especially under aggressive acceleration. Without additional training, FoCa achieves near-lossless speedups of 5.50× on FLUX, 6.45× on HunyuanVideo, 3.17× on Inf-DiT, and maintains high quality with a 4.53× speedup on DiT.

Shijun Zheng, Yu Guo, Weiquan Liu, Yu Zang, Siqi Shen, Ming Cheng, Cheng Wang

3D object detection in adverse weather is crucial for autonomous driving, especially in smoke where LiDAR data becomes sparse and noisy. Due to the lack of real smoke data, this paper introduces a physics-based simulation framework to generate realistic LiDAR point clouds of smoke and augment large-scale driving datasets. First, we present a 3D fluid dynamics-based smoke simulation framework in Unity, which models the realistic spatial diffusion and temporal evolution of smoke particles. Coupled with a physically accurate LiDAR perception module, our system captures complex light interactions—such as beam attenuation, scattering, and multi-path effects—to generate high-fidelity, physically consistent smoke point clouds. Second, we propose a range image-based data fusion strategy that seamlessly integrates the simulated smoke point clouds into large-scale real-world LiDAR datasets (e.g., Waymo). This approach accurately emulates LiDAR scanning characteristics and naturally incorporates occlusion effects, enabling realistic smoke integration without compromising spatial consistency. To validate our approach, we collect a real-world LiDAR smoke dataset (LiSmoke) and conduct extensive experiments using state-of-the-art 3D detectors. Results demonstrate that models trained with our augmented synthetic data achieve significant improvements in smoke-affected scenarios, while maintaining competitive performance in clear-weather conditions. Our work provides a cost-effective solution for enhancing perception robustness in safety-critical environments.

Longhui Zheng, Qiming Xia, Xiaolu Chen, Zhaoliang Liu, Chenglu Wen

3D object detection is critical for autonomous driving, yet it remains fundamentally challenging to simultaneously maximize computational efficiency and capture long-range spatial dependencies.We observed that Mamba-based models, with their linear state-space design, capture long-range dependencies at lower cost, offering a promising balance between efficiency and accuracy.However, existing methods rely on axis-aligned scanning within a fixed window, inevitably discarding spatial information. To address this problem, we propose WinMamba, a novel Mamba-based 3D feature-encoding backbone composed of stacked WinMamba blocks. To enhance the backbone with robust multi-scale representation, the WinMamba block incorporates a window-scale-adaptive module that compensates voxel features across varying resolutions during sampling. Meanwhile, to obtain rich contextual cues within the linear state space, we equip the WinMamba layer with a learnable positional encoding and a window-shift strategy.Extensive experiments on the KITTI and Waymo datasets demonstrate that WinMamba significantly outperforms the baseline. Ablation studies further validate the individual contributions of the WSF and AWF modules in improving detection accuracy. The code will be made publicly available.

Long Zheng, Zhi Li, Weidong Wang, Zhenyu Dai, Shuyun Li

Foundation segmentation models, such as SAM and its video-oriented variant SAM2, have achieved remarkable success in natural image and video segmentation. However, their direct application to echocardiography video is challenged by structural uncertainty arising from severe speckle noise and blurry anatomical boundaries. To address this, we propose E³SAM2, a lightweight adaptation framework that introduces a novel entropy-based methodology to explicitly model and mitigate such uncertainty. Specifically, an entropy-guided attention mechanism is introduced to steer the model’s focus toward structurally reliable features, particularly in speckle-dominated regions. Additionally, an entropy regularization loss is introduced to further enhance target-background discrimination. To better resolve indistinct anatomical contours, an edge-aware supervision module is incorporated to inject explicit boundary priors for sharper delineation. These components are efficiently integrated through a global-local feature adapter. Experiments on CAMUS and EchoNet-Dynamic datasets demonstrate that E³SAM2 achieves state-of-the-art segmentation and clinical estimation performance, while maintaining high computational efficiency.

Li Zheng, Liangbin Xie, Jiantao Zhou, He YiMin

Stable Diffusion (SD) often produces degraded outputs when the training dataset contains adversarial noise. Adversarial purification offers a promising solution by removing adversarial noise from contaminated data. However, existing purification methods are primarily designed for classification tasks and fail to address SD-specific adversarial strategies, such as attacks targeting the VAE encoder, UNet denoiser, or both. To address the gap in SD security, we propose Universal Diffusion Adversarial Purification (UDAP), a novel framework tailored for defending adversarial attacks targeting SD models. UDAP leverages the distinct reconstruction behaviors of clean and adversarial images during Denoising Diffusion Implicit Models (DDIM) inversion to optimize the purification process. By minimizing the DDIM metric loss, UDAP can effectively remove adversarial noise. Additionally, we introduce a dynamic epoch adjustment strategy that adapts optimization iterations based on reconstruction errors, significantly improving efficiency without sacrificing purification quality. Experiments demonstrate UDAP’s robustness against diverse adversarial methods, including PID (VAE-targeted), Anti-DreamBooth (UNet-targeted), MIST (hybrid), and robustness-enhanced variants like Anti-Diffusion (Anti-DF) and MetaCloak. UDAP also generalizes well across SD versions and text prompts, showcasing its practical applicability in real-world scenarios.

Huangqimei Zheng, Chengyi Pan, Qian Jiang, Wei Zhou, Xin Jin

Pan-sharpening aims to generate high-resolution multispectral images by integrating the spectral richness of low-resolution multispectral images with the spatial details of high-resolution panchromatic images. Although frequency-domain modeling shows great potential in this field, most existing methods are still limited to spatial-domain processing or fail to effectively capture the contextual interactions between frequency and spatial features. To address these issues, we propose a novel multi-scale frequency-spatial collaborative fusion approach. A Frequency-Spatial U-Net is introduced as the backbone network, in which frequency-spatial modeling blocks are embedded to progressively enhance the frequency-guided spatial contextual modeling capability across layers. To this end, we design a Dual Branch Frequency Attention module that adaptively enhances high- and low-frequency information. In addition, we introduce fine-mid-coarse resolution branches and devise a main-auxiliary multi-scale reconstruction loss to facilitate collaborative optimization. The effectiveness of the proposed model is validated through extensive experiments, demonstrating superior performance in both qualitative and quantitative evaluations. Moreover, our model achieves the fastest inference time among all compared methods, striking an excellent balance between accuracy and efficiency.

Haowen Zheng, Hu Zhu, Lu Deng, Weihao Gu, Yang Yang, Yanyan Liang

Camera-based temporal 3D object detection has shown impressive results in autonomous driving, with offline models improving accuracy by using future frames. Knowledge distillation (KD) can be an appealing framework for transferring rich information from offline models to online models. However, existing KD methods overlook future frames, as they mainly focus on spatial feature distillation under strict frame alignment or on temporal relational distillation, thereby making it challenging for online models to effectively learn future knowledge. To this end, we propose a sparse query-based approach, Future Temporal Knowledge Distillation (FTKD), which effectively transfers future frame knowledge from an offline teacher model to an online student model. Specifically, we present a future-aware feature reconstruction strategy to encourage the student model to capture future features without strict frame alignment. In addition, we further introduce future-guided logit distillation to leverage the teacher's stable foreground and background context. FTKD is applied to two high-performing 3D object detection baselines, achieving up to 1.3 mAP and 1.3 NDS gains on the nuScenes dataset, as well as the most accurate velocity estimation, without increasing inference cost.