论文检索

Liuhan Yin, Runkun Ju, Guodong Guo, Erkang Cheng

Unlike discriminative approaches in autonomous driving that predict a fixed set of candidate trajectories of the ego vehicle, generative methods, such as diffusion models, learn the underlying distribution of future motion, enabling more flexible trajectory prediction. However, since these methods typically rely on denoising human-craft trajectory anchors or random noise, there remains significant room for improvement. In this paper, we propose DiffRefiner, a novel two-stage trajectory prediction framework. The first stage employs a transformer-based Proposal Decoder to generate coarse trajectory predictions by regressing from sensor inputs using predefined trajectory anchors. The second stage applies a Diffusion Refiner that iteratively denoises and refines these initial predictions. In this way, we enhance the performance of diffusion-based planning by incorporating a discriminative trajectory proposal module, which provides strong guidance for the generative refinement process. Furthermore, we design a fine-grained denoising decoder to enhance scene compliance, enabling more accurate trajectory prediction through enhanced alignment with the surrounding environment. Experimental results demonstrate that DiffRefiner achieves state-of-the-art performance, attaining 87.4 EPDMS on NAVSIM v2, and 87.1 DS along with 71.4 SR on Bench2Drive, thereby setting new records on both public benchmarks. The effectiveness of each component is validated via ablation studies as well.

Chun-Hsiao Yeh, Yilin Wang, Nanxuan Zhao, Richard Zhang, Yuheng Li, Yi Ma, Krishna Kumar Singh

Recent diffusion-based image editing methods have made great strides in text-guided tasks but often struggle with complex, indirect instructions. Additionally, current models frequently exhibit poor identity preservation, unintended edits, or rely on manual masks. To overcome these limitations, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that bridges user intent with editing model capabilities. X-Planner uses chain-of-thought reasoning to systematically break down complex instructions into simpler sub-instructions. For each one, X-Planner automatically generates precise edit types and segmentation masks, enabling localized, identity-preserving edits without applying external tools or models during inference. To enable the training of such a planner, we also introduce a fully automated, reproducible pipeline to generate large-scale, high-quality training data. Our complete system achieves state-of-the-art results on both existing and newly proposed complex instruction-based editing benchmarks.

Yulin Yao, Kangfeng Zheng, Bin Wu, Chunhua Wu, Jujie Wang, Jiaqi Gao, Minjiao Yang, Dan Luo

Driven by advances in GANs and diffusion models, deepfake content has reached an unprecedented level of photorealism, causing detectors to deteriorate once they leave their training domain. Most prior studies adopt CLIP as the backbone of an image-level binary classifier, yet overlook CLIP’s core strength: text-to-image semantic alignment. Moreover, captions generated by CLIP-CAP lack sufficient high-level semantics to distinguish between authentic and manipulated faces. Deepfake generators often fail to maintain semantic coherence, resulting in contradictions that traditional visual models cannot capture. Existing approaches also intermingle all samples during training and thus lack a systematic, difficulty-aware curriculum. To bridge these gaps, we introduce Semantic- and Frequency-Enhanced (SAFE) deepfake detection, a two-component framework: 1) Semantic-enhanced multimodal alignment. Authenticity cues are injected into CLIP-CAP captions, and low-rank LoRA fine-tuning is applied to CLIP’s visual branch, yielding dual supervision for text–image alignment and forgery discrimination. 2) Dual-score curriculum learning. Fourier Correlation Variance (FCV) measures local spectral consistency and, combined with the loss value, is transformed into a difficulty score that ranks training samples from easy to hard, reducing training time by 23.3% and enhancing generalization. SAFE attains state-of-the-art performance on several cross-dataset and cross-manipulation benchmarks. Ablation studies confirm that semantic enhancement, LoRA fine-tuning, and dual-score curriculum are complementary, jointly delivering substantial gains in open-set generalization.

Wei Yao, Yunlian Sun, Hongwen Zhang, Yebin Liu, Jinhui Tang

Generating high-fidelity full-body human interactions with dynamic objects and static scenes remains a critical challenge in computer graphics and animation. Existing methods for human-object interaction often neglect scene context, leading to implausible penetrations, while human-scene interaction approaches struggle to coordinate fine-grained manipulations with long-range navigation. To address these limitations, we propose HOSIG, a novel framework for synthesizing full-body interactions through hierarchical scene perception. Our method decouples the task into three key components: 1) a scene-aware grasp pose generator that ensures collision-free whole-body postures with precise hand-object contact by integrating local geometry constraints, 2) a heuristic navigation algorithm that autonomously plans obstacle-avoiding paths in complex indoor environments via compressed 2D floor maps and dual-component spatial reasoning, and 3) a scene-guided motion diffusion model that generates trajectory-controlled, full-body motions with finger-level accuracy by incorporating spatial anchors and dual-space gradient-based guidance. Extensive experiments on the TRUMANS dataset demonstrate superior performance over state-of-the-art methods. Notably, our framework supports unlimited motion length through autoregressive generation and requires minimal manual intervention. This work bridges the critical gap between scene-aware navigation and dexterous object manipulation, advancing the frontier of embodied interaction synthesis.

Yue Yang, Song Tang, Qijun Zhao, Hailun Zhang, Xiwen Wang, Zijian Deng

The high cost of synthetic aperture radar (SAR) data acquisition motivates SAR image generation research. However, the data scarcity and SAR's inherent azimuth sensitivity make generative models suffer from severe azimuth overfitting. Most existing methods require supplementary data to work effectively, limiting their practicality. In this paper, we propose SAR-DisentDM, a novel semantic-disentangled diffusion model for limited-data SAR image generation, without requiring any auxiliary resources. We develop a physics-aware diffusion architecture that explicitly models semantic knowledge of SAR images, including intrinsic characteristics, contextual diversity, and measurement randomness. A key innovation is the attention-guided semantic disentanglement (AGSD) module, designed to decouple category-specific features from azimuth-variable scattering patterns. This is achieved by aid of a dual disentangled loss with time-step-adaptive optimization. Furthermore, we introduce an azimuth angle perturbation augmentation (AAPA) mechanism, to enhance the model's robustness to minor azimuth angle errors. Extensive evaluations validate that SAR-DisentDM enables controllable SAR image synthesis with designated attributes, significantly improving representation and generalization abilities under limited data. Synthetic imagery from our approach boosts automatic target recognition (ATR) accuracy beyond state-of-the-art methods.

Yitong Yang, Yinglin Wang, Changshuo Wang, Huajie Wang, Shuting He

The success of 3DGS in generative and editing applications has sparked growing interest in 3DGS-based style transfer. However, current methods still face two major challenges: (1) multi-view inconsistency often leads to style conflicts, resulting in appearance smoothing and distortion; and (2) heavy reliance on VGG features, which struggle to disentangle style and content from style images, often causing content leakage and excessive stylization. To tackle these issues, we introduce FantasyStyle, a 3DGS-based style transfer framework, and the first to rely entirely on diffusion model distillation. It comprises two key components: (1) Multi-View Frequency Consistency. We enhance cross-view consistency by applying a 3D filter to multi-view noisy latent, selectively reducing low-frequency components to mitigate stylized prior conflicts. (2) Controllable Stylized Distillation. To suppress content leakage from style images, we introduce negative guidance to exclude undesired content. In addition, we identify the limitations of Score Distillation Sampling and Delta Denoising Score in 3D style transfer and remove the reconstruction term accordingly. Building on these insights, we propose a controllable stylized distillation that leverages negative guidance to more effectively optimize the 3D Gaussians. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches, achieving higher stylization quality and visual realism across various scenes and styles.

Yifan Yang, Zhi Cen, Sida Peng, Xiangwei Chen, Yifu Deng, Xinyu Zhu, Fan Jia, Xiaowei Zhou, Hujun Bao

This paper focuses on the task of speech-driven 3D facial animation, which aims to generate realistic and synchronized facial motions driven by speech inputs. Recent methods have employed audio-conditioned diffusion models for 3D facial animation, achieving impressive results in generating expressive and natural animations. However, these methods process the whole audio sequences in a single pass, which poses two major challenges: they tend to perform poorly when handling audio sequences that exceed the training horizon and will suffer from significant latency when processing long audio inputs. To address these limitations, we propose a novel autoregressive diffusion model that outputs facial motions in a streaming manner. This design ensures flexibility with varying audio lengths and achieves low latency independent of audio duration. Specifically, we select a limited number of past frames as historical motion context and combine them with the audio input to create a dynamic condition. This condition guides a lightweight diffusion head to iteratively generate facial motion frames, enabling real-time synthesis with high-quality results. Experiments conducted on public datasets demonstrate that our approach outperforms recent baseline methods.

Tingyu Yang, Jue Gong, Jinpei Guo, Wenbo Li, Yong Guo, Yulun Zhang

JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoration methods have made considerable progress, they often struggle to recover complex texture details, resulting in over-smoothed outputs. To overcome these limitations, we propose SODiff, a novel and efficient semantic-oriented one-step diffusion model for JPEG artifacts removal. Our core idea is that effective restoration hinges on providing semantic-oriented guidance to the pre-trained diffusion model, thereby fully leveraging its powerful generative prior. To this end, SODiff incorporates a semantic-aligned image prompt extractor (SAIPE). SAIPE extracts rich features from low-quality (LQ) images and projects them into an embedding space semantically aligned with that of the text encoder. Simultaneously, it preserves crucial information for faithful reconstruction. Furthermore, we propose a quality factor-aware time predictor that implicitly learns the compression quality factor (QF) of the LQ image and adaptively selects the optimal denoising start timestep for the diffusion process. Extensive experimental results show that our SODiff outperforms recent leading methods in both visual quality and quantitative metrics.

Ting Yang, Siyu Yang, Xiyao Liu, Songtao Wu, Gerald Schaefer, Kuanhong Xu, Hui Fang

Arbitrary style transfer (AST), a popular AI-powered photo editing function, aims to strike an optimal balance between content and style injection from two images in order to generate a novel high-fidelity stylised image. Recently, diffusion models have been applied to AST due to their high generation quality as well as flexibility to embed conditions. However, these models are still not satisfactory and may exhibit inferior performance compared to non-diffusion based methods. This is due to the diffusion process not being purposely designed for AST, leading to suboptimal solutions to trade-off content preservation and style embedding. In this paper, we propose ACID-Style, a novel adaptive condition injection diffusion-based AST framework for improved content/style feature injection to address this research challenge. Using two lightweight adapters, a content and a style injection module, and an adaptive injection mechanism, our approach is able to fully exploit a pre-trained stable diffusion model for AST-specific adaptation and our diffusion model thus learns the most effective timing for content and style injection in the diffusion sampling process. Comprehensive evaluations demonstrate that our method achieves superior style transfer performance, both quantitatively and qualitatively, compared to other state-of-the-art style transfer methods.

Rui Yang, Yunfei Bai, Yuehua Liu, Xiaomao Li, Shaorong Xie

In semi-supervised semantic segmentation (SSSS), segmentation performance is heavily constrained by the quality of pseudo labels. However, prevalent pseudo-label optimization approaches rely on the model’s internal self-correction. When the model fails to recognize or adequately represent certain classes, this self-enhancement mechanism amplifies initial mistakes, ultimately leading to poor semantic or spatial consistency. To address this limitation, we propose ViLaDiff to enhance pseudo-label quality. Specifically, ViLaDiff first employs a prompt-guided image captioning task to generate descriptive text for each input image, providing high-level semantic context. To our knowledge, this is the first attempt to introduce vision-language modeling into SSSS. We design a vision-language fusion module to enhance feature semantics and discriminative capability. It integrates cross-modal interactions with dual-path knowledge to ensure semantic consistency. Additionally, while language provides high-level semantic guidance, it is inherently limited in expressing fine-grained spatial structures. Therefore, we propose an edge-aware mixed-noise diffusion process. It simulates feature-level uncertainty through Gaussian perturbations and introduces class-flipping noise into the masks to model misclassification errors. To enhance boundary refinement, we apply a higher flipping probability along mask edges, enabling edge-aware modeling during denoising. Extensive experiments on public benchmarks validate that our method significantly improves pseudo-label quality and segmentation performance.

Qinhong Yang, Dongdong Chen, Qi Chu, Tao Gong, Qiankun Liu, Zhentao Tan, Xulin Li, Huamin Feng, Nenghai Yu

Recent diffusion-based models have significantly improved inpainting quality. However, existing methods struggle with multi-task inpainting due to conflicting optimization objectives, and current datasets are typically limited to task-specific scenarios, hindering joint training. To address these challenges, we propose MagicPaint, a unified diffusion-based inpainting model that supports object addition, removal, and unconditional inpainting across both text and image modalities. MagicPaint semantically decouples operation types and target content by learnable tokens in MMToken Module, effectively reconciling conflicting optimization objectives and enabling robust multi-task, multi-modal inpainting. Besides, a novel inpainting paradigm named MagicMask, encodes operating intent directly into the mask and applies a mask loss for spatially precise supervision. In addition, existing inpainting datasets are insufficient for multi-task and multi-modal scenarios, limiting the capability of inpainting models. Thus, we further introduce a new dataset comprising 2.1M image tuples. It is dedicatedly designed to support diverse inpainting scenarios and significantly improves upon existing datasets, particularly in object removal. Through efforts from both model and data perspectives, MagicPaint enables users to operate anything—add, remove or inpaint content which is specified through either text or image modalities in a seamless and unified manner. Extensive experiments demonstrate that MagicPaint achieves state-of-the-art performance across three key tasks (i.e., text-guided addition, image-guided addition, and object removal) and produces outputs with superior visual consistency and contextual fidelity compared to existing methods.

Kaixiang Yang, Boyang Shen, Xin Li, Yuchen Dai, Yuxuan Luo, Yueran Ma, Wei Fang, Qiang Li, Zhiwei Wang

Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies, and over-editing due to the lack of effective integration of source information. In this paper, we present FIA-Edit, a novel inversion-free framework that achieves high-fidelity and semantically precise edits through a Frequency-Interactive Attention. Specifically, we design two key components: (1) a Frequency Representation Interaction (FRI) module that enhances cross-domain alignment by exchanging frequency components between source and target features within self-attention, and (2) a Feature Injection (FIJ) module that explicitly incorporates source-side queries, keys, values, and text embeddings into the target branch's cross-attention to preserve structure and semantics. Comprehensive and extensive experiments demonstrate that FIA-Edit supports high-fidelity editing at low computational cost (~6s per 512 * 512 image on an RTX 4090) and consistently outperforms existing methods across diverse tasks in visual quality, background fidelity, and controllability. Furthermore, we are the first to extend text-guided image editing to clinical applications. By synthesizing anatomically coherent hemorrhage variations in surgical images, FIA-Edit opens new opportunities for medical data augmentation and delivers significant gains in downstream bleeding classification.

Qinglong Yan, Tong Zou, Xunpeng Yi, Xinyu Xiang, Xuying Wu, Hao Zhang, Jiayi Ma

Recent advances in naturalistic physical adversarial patch generation show great promise in protecting personal privacy against detector-based malicious surveillance while remaining inconspicuous to human observers. In this work, we present the first systematic categorization and in-depth re-examination of existing methods into three representative paradigms, revealing a pervasive imbalance: enforcing naturalness constraints inherently restricts the adversarial search space, thus limiting attack performance. To address this challenge, we propose a novel paradigm based on class-optimized diffusion, termed Diff-NAT. Diff-NAT leverages pretrained diffusion models as powerful natural image priors and introduces a unified iterative framework that jointly optimizes two complementary components: semantic-level textual prompts and instance-level latent codes. Specifically, prompt optimization enables broad traversal across inter-class semantic regions, while latent refinement allows for fine-grained manipulation within class objectives. This dual-level optimization facilitates progressive navigation toward adversarial distributions embedded within the natural semantic manifold. Extensive experiments in both digital and physical settings demonstrate that Diff-NAT outperforms existing SOTA approaches in terms of both visual realism and aggressiveness.

Zhou Xu, Qi Wang, Yuxiao Yang, Luyuan Zhang, Zhang Liang, Yang Li

Score Distillation Sampling (SDS) enables 3D asset generation by distilling priors from pretrained 2D text-to-image diffusion models, but vanilla SDS suffers from over-saturation and over-smoothing. To mitigate this issue, recent variants have incorporated negative prompts. However, these methods face a critical trade-off: limited texture optimization, or significant texture gains with shape distortion. In this work, we first conduct a systematic analysis and reveal that this trade-off is fundamentally governed by the utilization of the negative prompts, where Target Negative Prompts (TNP) that embed target information in the negative prompts dramatically enhancing texture realism and fidelity but inducing shape distortions. Informed by this key insight, we introduce the Target-Balanced Score Distillation (TBSD). It formulates generation as a multi-objective optimization problem and introduces an adaptive strategy that effectively resolves the aforementioned trade-off. Extensive experiments demonstrate that TBSD significantly outperforms existing state-of-the-art methods, yielding 3D assets with high-fidelity textures and geometrically accurate shape.

Hang Xu, Yang Xiao, Changlong Jiang, Haohong Kuang, Kaidi Zhang, Min Du, Ran Wang

In this paper, MoEG-HOI is proposed as a novel method for the challenging 3D hand-object interaction (HOI) motion generation task, by introducing Mixture-of-Experts (MoE) to this field for the first time. Almost all the mainstream approaches in HOI motion generation leverage diffusion model as its strong generative ability. Nevertheless, due to HOI’s fine-grained property, well training diffusion in one-stage way is actually not trivial. Existing state-of-the-art (SOTA) methods (e.g.,Text2HOI and MF-MDM) alleviate this mainly via a coarse-to-fine, multi-stage paradigm. Although effective and practical, this paradigm prevents end-to-end training for optimal performance. In contrast, MoEG-HOI applies MoE to address this in one-stage way, with end-to-end training ability. This allows each expert to specialize in certain distinct HOI patterns, which alleviates individual expert’s training difficulty. However, intuitively applying MoE is not optimal due to the issues of: (1) towards expert design, original MoE cannot well characterize hand’s articulated structure at the levels of hand, finger, and joint explicitly, and (2) for expert routing mechanism, the characteristics of variational HOI action classes and diffusion noise levels have not been concerned. Towards the first problem, MoE’s experts are designed into groups that correspond to motion generation for hand, finger, and joint respectively, under the semantic guidance from global to local. To facilitate this, HOI’s text description will be correspondingly refined at Hand-Finger-Joint levels using LLM. Secondly, during MoE routing, the information of HOI’s action label and diffusion noise level is concerned to select experts jointly, to better reveal actions’ inter-class variation and dynamics of diffusion generation. SOTA performance on ARCTIC, GRAB and H2O datasets demonstrates the effectiveness of our method.

Haijun Xiong, Bin Feng, Bang Wang, Xinggang Wang, Wenyu Liu

Gait recognition offers a non-intrusive biometric solution by identifying individuals through their walking patterns. Although discriminative models have achieved notable success in this domain, the full potential of generative models remains largely unexplored. In this paper, we introduce CoD², a novel framework that combines the data distribution modeling capabilities of diffusion models with the semantic representation learning strengths of discriminative models to extract robust gait features. We propose a Multi-level Conditional Control strategy that integrates both high-level identity-aware semantic conditions and low-level visual details. Specifically, the high-level condition, extracted by the discriminative extractor, guides the generation of identity-consistent gait sequences, while low-level visual details, such as appearance and motion, are preserved to enhance consistency. Moreover, the generated sequences facilitate the discriminative extractor's learning, enabling it to capture more comprehensive high-level semantic features. Extensive experiments on four datasets (SUSTech1K, CCPG, GREW, and Gait3D) demonstrate that CoD² achieves state-of-the-art performance and can be seamlessly integrated with existing discriminative methods, yielding consistent improvements.

Jingwei Xin, Kai Guo, Jie Li, Nannan Wang

Cloud removal (CR) in remote sensing imagery is a critical yet challenging task due to complex cloud patterns and diverse underlying ground structures. Despite recent progress in generative models such as diffusion models, CR remains limited by their inadequate capability to perceive and reconstruct structured information beneath cloud-covered areas. In this work, we propose a Visibility-guided Semantic Estimation and Reconstruction network for cloud removal (VISER-CR), which reformulates CR as a structure-guided completion problem. Specifically, VISER-CR explicitly models cloud interference via spatial masking, encouraging the model to reason beyond pixel-level appearance and enhance scene-level structural understanding. Moreover, to further improve the representation of structural information, we introduce Patch Saliency Encoding, a self-guided mechanism that implicitly models structural alignment among patches, significantly enhancing clustering consistency and semantic separability in the latent space. This adaptive mechanism guides the network to focus on learning and reconstructing structurally important regions, thereby reducing redundancy and improving overall cloud removal performance. Extensive experiments on multiple benchmark datasets demonstrate the superior effectiveness of our method.

Weixing Xie, Ying Ye, Xian Wu, Jintian Li, Bingchuan Li, Yanchen Lin, Junfeng Yao

Reconstructing dynamic objects from monocular RGB-D video is critical for advancing 3D vision applications and enhancing user experience. However, monocular RGB-D video provides limited 3D observations, making the reconstruction of unobserved regions highly under-constrained. Despite recent advances that combine neural implicit surfaces with diffusion models, the inherent limitations of implicit representations and the lack of effective guidance in diffusion priors lead to blurry appearance and inaccurate geometry in dynamic object reconstruction. To address the issue, we present MGD, which leverages scene-adaptive diffusion priors and Mesh-guided Gaussians for realistic rendering and geometrically accurate reconstruction of dynamic objects, including unobserved regions. The dynamic 3D objects reconstructed by MGD are represented using our proposed Mesh-guided Gaussians, which leverage global and local Gaussians to capture large-scale deformations and fine-grained appearance details, respectively. Additionally, in order to utilize depth information, we integrate a depth ControlNet into the diffusion model and conduct scene-adaptive fine-tuning. We design a self-generated image-pair strategy to produce image pairs used for fine-tuning. Extensive experiments demonstrate that MGD achieves state-of-the-art performance in both high-fidelity reconstruction and structural completeness, while maintaining real-time efficiency during training and rendering.

Tengfei Xiao, Yue Wu, Zhigang Gao, Yongzhe Yuan, Can Qin, Hao Li, Mingyang Zhang

Human Novel View Synthesis (HNVS) aims to synthesize photorealistic human images from novel viewpoints given observations from known views. Despite significant advances achieved by existing methods such as NeRF, diffusion models, and 3DGS, they still face substantial challenges in achieving stable modeling from a single image. In this paper, we introduce Dual-Constraint Human Gaussian Splatting (DcSplat), a novel, simple, and efficient 3D Gaussian-based framework for single-view 3D human reconstruction. To address occlusion-induced texture missing and depth ambiguities, we introduce two key components: a Latent Multi-View Consistency Constraint Mechanism and a Geometric Constraint Module. The former employs a Latent-space Appearance Transformer (LatentFormer) to learn semantically coherent, view-consistent appearance priors via SMPL-guided pseudo-view fusion. The latter refines noisy SMPL-based depth through a U-Net-like structure conditioned on latent appearance features. These two modules are jointly optimized to generate high-quality Gaussian parameters in a unified latent space. Extensive experiments demonstrate that DcSplat outperforms existing SOTA methods in both geometry and texture quality, while achieving fast inference and lower computational cost.

Yichong Xia, Yimin Zhou, Jinpeng Wang, Bin Chen

Recent advancements in diffusion-based generative priors have enabled visually plausible image compression at extremely low bit rates. However, existing approaches suffer from slow sampling processes and suboptimal bit allocation due to fragmented training paradigms. In this work, we propose Accelerate Diffusion-based Image Compression via Consistency Prior Refinement (DiffCR), a novel compression framework for efficient and high-fidelity image reconstruction. At the heart of DiffCR is a Frequency-aware Skip Estimation (FaSE) module that refines the epsilon-prediction prior from a pre-trained latent diffusion model and aligns it with compressed latents at different timesteps via Frequency Decoupling Attention (FDA). Furthermore, a lightweight consistency estimator enables fast two-step decoding by preserving the semantic trajectory of diffusion sampling. Without updating the backbone diffusion model, DiffCR achieves substantial bitrate savings (27.2% BD-rate(LPIPS) and 65.1% BD-rate(PSNR)) and over 10 times speed-up compared to SOTA diffusion-based compression baselines.