GenAI4ELab/papercli-papers huggingface dataset:
From One to More: Contextual Part Latents for 3D Generation
Authors: Shaocong Dong; Lihe Ding; Xiao Chen; Yaokun Li; Yuxin Wang; Yucheng Wang; Qi Wang; Jaehyeok Kim; Chenjian Gao; Zhanpeng Huang; Zibin Wang; Tianfan Xue; Dan Xu
Venue: ICCV 2025 (main)

Abstract: To generate 3D objects, early research focused on multi-view-driven approaches relying solely on 2D renderings. Recently, the 3D native latent diffusion paradigm has demonstrated superior performance in 3D generation, because it fully leverages the geometric information provided in ground truth 3D data. Despite its fast development, 3D diffusion still faces three challenges. First, the majority of these methods represent a 3D object by one single latent, regardless of its complexity. This may lead to detail loss when generating 3D objects with multiple complicated parts. Second, most 3D assets are designed parts by parts, yet the current holistic latent representation overlooks the independence of these parts and their interrelationships, limiting the model's generative ability. Third, current methods rely on global conditions (e.g., text, image, point cloud) to control the generation process, lacking detailed controllability. Therefore, motivated by how 3D designers create a 3D object, we present a new part-based 3D generation framework, CoPart, which represents a 3D object with multiple contextual part latents and simultaneously generates coherent 3D parts. This part-based framework has several advantages, including: i) reduces the encoding burden of intricate objects by decomposing them into simpler parts, ii) facilitates part learning and part relationship modeling, and iii) naturally supports part-level control. Furthermore, to ensure the coherence of part latents and to harness the powerful priors from foundation models, we propose a novel mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising. Benefiting from the part-based representation, we demonstrate that CoPart can support various applications including part-editing, articulated object generation, and mini-scene generation. Moreover, we collect a new large-scale 3D part dataset named Partverse from Objaverse through automatic mesh segmentation and subsequent human post-annotations. By training on the proposed dataset, CoPart achieves promising part-based 3D generation with high controllability. Project page: https://hkdsc.github.io/project/copart.
CaptionSmiths: Flexibly Controlling Language Pattern in Image Captioning
Authors: Kuniaki Saito; Donghyun Kim; Kwanyong Park; Atsushi Hashimoto; Yoshitaka Ushiku
Venue: ICCV 2025 (main)

Abstract: An image captioning model flexibly switching its language pattern, e.g., descriptiveness and length, should be useful since it can be applied to diverse applications. However, despite the dramatic improvement in generative vision-language models, fine-grained control over the properties of generated captions is not easy due to two reasons: (i) existing models are not given the properties as a condition during training and (ii) existing models cannot smoothly transition its language pattern from one state to the other. Given this challenge, we propose a new approach, CaptionSmiths, to acquire a single captioning model that can handle diverse language patterns. First, our approach quantifies three properties of each caption, length, descriptiveness, and uniqueness of a word, as continuous scalar values, without human annotation. Given the values, we represent the conditioning via interpolation between two endpoint vectors corresponding to the extreme states, e.g., one for a very short caption and one for a very long caption. Empirical results demonstrate that the resulting model can smoothly change the properties of the output captions and show higher lexical alignment than baselines. For instance, CaptionSmiths reduces the error in controlling caption length by 506% despite better lexical alignment. Code will be available on https://github.com/omronsinicx/captionsmiths.
CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression Segmentation
Authors: Zhuoyan Luo; Yinghao Wu; Tianheng Cheng; Yong Liu; Yicheng Xiao; Hongfa Wang; Xiao-Ping Zhang; Yujiu Yang
Venue: ICCV 2025 (main)

Abstract: The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approaches address GRES by directly extending the well-adopted RES frameworks with object-existence identification. However, these approaches tend to encode multi-granularity object information into a single representation, which makes it difficult to precisely represent comprehensive objects of different granularity. Moreover, the simple binary object-existence identification across all referent scenarios fails to specify their inherent differences, incurring ambiguity in object understanding. To tackle the above issues, we propose a **Co**unting-Aware **H**ierarchical **D**ecoding framework (CoHD) for GRES. By decoupling the intricate referring semantics into different granularity with a visual-linguistic hierarchy, and dynamic aggregating it with intra- and inter-selection, CoHD boosts multi-granularity comprehension with the reciprocal benefit of the hierarchical nature. Furthermore, we incorporate the counting ability by embodying multiple/single/non-target scenarios into count- and category-level supervision, facilitating comprehensive object perception. Experimental results on gRefCOCO, Ref-ZOM, R-RefCOCO, and RefCOCO benchmarks demonstrate the effectiveness and rationality of CoHD which outperforms state-of-the-art GRES methods by a remarkable margin. Code is available.
PersonalVideo: High ID-Fidelity Video Customization without Dynamic and Semantic Degradation
Authors: Hengjia Li; Haonan Qiu; Shiwei Zhang; Xiang Wang; Yujie Wei; Zekun Li; Yingya Zhang; Boxi Wu; Deng Cai
Venue: ICCV 2025 (main)

Abstract: The current text-to-video (T2V) generation has made significant progress in synthesizing realistic general videos, but it is still under-explored in identity-specific human video generation with customized ID images. The key challenge lies in maintaining high ID fidelity consistently while preserving the original motion dynamic and semantic following after the identity injection. Current video identity customization methods mainly rely on reconstructing given identity images on text-to-image models, which have a divergent distribution with the T2V model. This process introduces a tuning-inference gap, leading to dynamic and semantic degradation. To tackle this problem, we propose a novel framework, dubbed PersonalVideo, that applies a mixture of reward supervision on synthesized videos instead of the simple reconstruction objective on images. Specifically, we first incorporate identity consistency reward to effectively inject the reference's identity without the tuning-inference gap. Then we propose a novel semantic consistency reward to align the semantic distribution of the generated videos with the original T2V model, which preserves its dynamic and semantic following capability during the identity injection. With the non-reconstructive reward training, we further employ simulated prompt augmentation to reduce overfitting by supervising generated results in more semantic scenarios, gaining good robustness even with only a single reference image.Extensive experiments demonstrate our method's superiority in delivering high identity faithfulness while preserving the inherent video generation qualities of the original T2V model, outshining prior methods.
MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration
Authors: Zhehui Wu; Yong Chen; Naoto Yokoya; Wei He
Venue: ICCV 2025 (main)

Abstract: Hyperspectral images (HSIs) often suffer from diverse and unknown degradations during imaging, leading to severe spectral and spatial distortions. Existing HSI restoration methods typically rely on specific degradation assumptions, limiting their effectiveness in complex scenarios. In this paper, we propose MP-HSIR, a novel multi-prompt framework that effectively integrates spectral, textual, and visual prompts to achieve universal HSI restoration across diverse degradation types and intensities. Specifically, we develop a prompt-guided spatial-spectral transformer, which incorporates spatial self-attention and a prompt-guided dual-branch spectral self-attention. Since degradations affect spectral features differently, we introduce spectral prompts in the local spectral branch to provide universal low-rank spectral patterns as prior knowledge for enhancing spectral reconstruction. Furthermore, the text-visual synergistic prompt fuses high-level semantic representations with fine-grained visual features to encode degradation information, thereby guiding the restoration process. Extensive experiments on 9 HSI restoration tasks, including all-in-one scenarios, generalization tests, and real-world cases, demonstrate that MP-HSIR not only consistently outperforms existing all-in-one methods but also surpasses state-of-the-art task-specific approaches across multiple tasks. The code and models are available at https://github.com/ZhehuiWu/MP-HSIR.
Learning A Unified Template for Gait Recognition
Authors: Panjian Huang; Saihui Hou; Junzhou Huang; Yongzhen Huang
Venue: ICCV 2025 (main)

Abstract: "What I cannot create, I do not understand." Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models have demonstrated remarkable semantic structural and memory capabilities in image generation, denoising, and restoration, which intuitively benefits representation learning. However, current gait networks rarely embrace this perspective, relying primarily on learning by contrasting gait samples under varying complex conditions, leading to semantic inconsistency and uniformity issues. To address these issues, we propose Origins with generative capabilities whose underlying philosophy is that different entities are generated from a unified template, inherently regularizing gait representations within a consistent and diverse semantic space to capture differences accurately. Admittedly, learning this unified template is exceedingly challenging, as it requires the comprehensiveness of the template to encompass gait representations with various conditions. Inspired by Diffusion Models, Origins diffuses the unified template into timestep templates for gait generative modeling, and meanwhile transfers the unified template for gait representation learning. Especially, gait generative modeling and representation learning serve as a unified framework to end-to-end joint training. Extensive experiments on CASIA-B, CCPG, SUSTech1K, Gait3D, GREW and CCGR-MINI demonstrate that Origins performs representation learning within a unified template, achieving superior performance.
LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation
Authors: Wei-Jer Chang; Wei Zhan; Masayoshi Tomizuka; Manmohan Chandraker; Francesco  Pittaluga
Venue: ICCV 2025 (main)

Abstract: Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj, a language-conditioned scene-diffusion model that simulates the joint behavior of all agents in traffic scenarios. By conditioning on natural language inputs, LangTraj provides flexible and intuitive control over interactive behaviors, generating nuanced and realistic scenarios. Unlike prior approaches that depend on domain-specific guidance functions, LangTraj incorporates language conditioning during training, facilitating more intuitive traffic simulation control. We propose a novel closed-loop training strategy for diffusion models, explicitly tailored to enhance stability and realism during closed-loop simulation. To support language-conditioned simulation, we develop Inter-Drive, a large-scale dataset with diverse and interactive labels for training language-conditioned diffusion models. Our dataset is built upon a scalable pipeline for annotating agent-agent interactions and single-agent behaviors, ensuring rich and varied supervision. Validated on the Waymo Motion Dataset, LangTraj demonstrates strong performance in realism, language controllability, and language-conditioned safety-critical simulation, establishing a new paradigm for flexible and scalable autonomous vehicle testing. Project website: https://langtraj.github.io/.
Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model
Authors: Daehee Park; Monu Surana; Pranav Desai; Ashish Mehta; Reuben MV John; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works addressed this by modifying model architectures, such as using hypernetworks. In contrast, we propose refining the training process to unlock each model's potential without altering its structure. We introduce Generative Active Learning for Trajectory prediction (GALTraj), the first method to successfully deploy generative active learning into trajectory prediction. It actively identifies rare tail samples where the model fails and augments these samples with a controllable diffusion model during training. In our framework, generating scenarios that are diverse, realistic, and preserve tail-case characteristics is paramount. Accordingly, we design a tail-aware generation method that applies tailored diffusion guidance to generate trajectories that both capture rare behaviors and respect traffic rules. Unlike prior simulation methods focused solely on scenario diversity, GALTraj is the first to show how simulator-driven augmentation benefits long-tail learning in trajectory prediction. Experiments on multiple trajectory datasets (WOMD, Argoverse2) with popular backbones (QCNet, MTR) confirm that our method significantly boosts performance on tail samples and also enhances accuracy on head samples.
REGEN: Learning Compact Video Embedding with (Re-)Generative Decoder
Authors: Yitian Zhang; Long Mai; Aniruddha Mahapatra; David Bourgin; Yicong Hong; Jonah Casebeer; Feng Liu; Yun Fu
Venue: ICCV 2025 (main)

Abstract: We present a novel perspective on learning video embedders for generative modeling: rather than requiring an exact reproduction of an input video, an effective embedder should focus on synthesizing visually plausible reconstructions. This relaxed criterion enables substantial improvements in compression ratios without compromising the quality of downstream generative models. Specifically, we propose replacing the conventional encoder-decoder video embedder with an encoder-generator framework that employs a diffusion transformer (DiT) to synthesize missing details from a compact latent space. Therein, we develop a dedicated latent conditioning module to condition the DiT decoder on the encoded video latent embedding. Our experiments demonstrate that our approach enables superior encoding-decoding performance compared to state-of-the-art methods, particularly as the compression ratio increases. To demonstrate the efficacy of our approach, we report results from our video embedders achieving a temporal compression ratio of up to 32x (8x higher than leading video emebbders) and validate the robustness of this ultra-compact latent space for text-to-video generation, providing a significant efficiency boost in latent diffusion model training and inference.
DADet: Safeguarding Image Conditional Diffusion Models against Adversarial and Backdoor Attacks via Diffusion Anomaly Detection
Authors: Hongwei Yu; Xinlong Ding; Jiawei Li; Jinlong Wang; Yudong Zhang; Rongquan Wang; Huimin Ma; Jiansheng Chen
Venue: ICCV 2025 (main)

Abstract: While image conditional diffusion models demonstrate impressive generation capabilities, they exhibit high vulnerability when facing backdoor and adversarial attacks. In this paper, we define a scenario named diffusion anomaly where the generated results of a reverse process under attack deviate significantly from the normal ones. By analyzing the underlying formation mechanism of the diffusion anomaly, we reveal how perturbations are amplified during the reverse process and accumulated in the results. Based on the analysis, we reveal the phenomena of divergence and homogeneity, which cause the diffusion process to deviate significantly from the normal process and to decline in diversity. Leveraging these two phenomena, we propose a method named Diffusion Anomaly Detection (DADet) to effectively detect both backdoor and adversarial attacks. Extensive experiments demonstrate that our proposal achieves excellent defense performance against backdoor and adversarial attacks. Specifically, for the backdoor attack detection, our method achieves an F1 score of 99% on different datasets, including MS COCO and CIFAR-10. For the detection of adversarial samples, the F1 score exceeds 84% across three adversarial attacks and two different tasks, evaluated on the MS COCO and Places365 datasets, respectively.
CL-Splats: Continual Learning of Gaussian Splatting with Local Optimization
Authors: Jan Ackermann; Jonas Kulhanek; Shengqu Cai; Haofei Xu; Marc Pollefeys; Gordon Wetzstein; Leonidas J. Guibas; Songyou Peng
Venue: ICCV 2025 (main)

Abstract: In dynamic 3D environments, accurately updating scene representations over time is crucial for applications in robotics, mixed reality, and embodied AI. As scenes evolve, efficient methods to incorporate changes are needed to maintain up-to-date, high-quality reconstructions without the computational overhead of re-optimizing the entire scene.This paper introduces CL-Splats, which incrementally updates Gaussian splatting-based 3D representations from sparse scene captures.CL-Splats integrates a robust change-detection module that segments updated and static components within the scene, enabling focused, local optimization that avoids unnecessary re-computation.Moreover, CL-Splats supports storing and recovering previous scene states, facilitating temporal segmentation and new scene-analysis applications.Our extensive experiments demonstrate that CL-Splats achieves efficient updates with improved reconstruction quality over the state-of-the-art. This establishes a robust foundation for future real-time adaptation in 3D scene reconstruction tasks.We will release our source code and the synthetic and real-world datasets we created to support further research in this area.
Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba for End-to-end Whole Slide Image Analysis
Authors: Zhongwei Qiu; Hanqing Chao; Tiancheng Lin; Wanxing Chang; Zijiang Yang; Wenpei Jiao; Yixuan Shen; Yunshuo Zhang; Yelin Yang; Wenbin Liu; Hui Jiang; Yun Bian; Ke Yan; Dakai Jin; Le Lu
Venue: ICCV 2025 (main)

Abstract: Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, the large size and complexity of WSIs may pose significant challenges for deep learning models, in both computational efficiency and effective representation learning. In this work, we introduce Pixel-Mamba, a novel deep learning architecture designed to efficiently handle gigapixel WSIs. Pixel-Mamba leverages the Mamba module, a state-space model (SSM) with linear memory complexity, and incorporates local inductive biases through progressively expanding tokens, akin to convolutional neural networks. This enables Pixel-Mamba to hierarchically combine both local and global information while efficiently addressing computational challenges. Remarkably, Pixel-Mamba achieves or even surpasses the quantitative performance of state-of-the-art (SOTA) foundation models that were pretrained on millions of WSIs or WSI-text pairs, in a range of tumor staging and survival analysis tasks, even without requiring any pathology-specific pretraining. Extensive experiments demonstrate the efficacy of Pixel-Mamba as a powerful and efficient framework for end-to-end WSI analysis.
Inpaint4Drag: Repurposing Inpainting Models for Drag-Based Image Editing via Bidirectional Warping
Authors: Jingyi Lu; Kai Han
Venue: ICCV 2025 (main)

Abstract: Drag-based image editing has emerged as a powerful paradigm for intuitive image manipulation. However, existing approaches predominantly rely on manipulating the latent space of generative models, leading to limited precision, delayed feedback, and model-specific constraints. Accordingly, we present Inpaint4Drag, a novel framework that decomposes drag-based editing into pixel-space bidirectional warping and image inpainting. Inspired by elastic object deformation in the physical world, we treat image regions as deformable materials that maintain natural shape under user manipulation. Our method achieves real-time warping previews (0.01s) and efficient inpainting (0.3s) at 512x512 resolution, significantly improving the interaction experience compared to existing methods that require minutes per edit. By transforming drag inputs directly into standard inpainting formats, our approach serves as a universal adapter for any inpainting model without architecture modification, automatically inheriting all future improvements in inpainting technology. Extensive experiments demonstrate that our method achieves superior visual quality and precise control while maintaining real-time performance. Project page: https://visual-ai.github.io/inpaint4drag
Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models
Authors: Zerui Tao; Yuhta Takida; Naoki Murata; Qibin Zhao; Yuki Mitsufuji
Venue: ICCV 2025 (main)

Abstract: Parameter-Efficient Fine-Tuning (PEFT) of text-to-image models has become an increasingly popular technique with many applications. Among the various PEFT methods, Low-Rank Adaptation (LoRA) and its variants have gained significant attention due to their effectiveness, enabling users to fine-tune models with limited computational resources. However, the approximation gap between the low-rank assumption and desired fine-tuning weights prevents the simultaneous acquisition of ultra-parameter-efficiency and better performance. To reduce this gap and further improve the power of LoRA, we propose a new PEFT method that combines two classes of adaptations, namely, transform and residual adaptations. In specific, we first apply a full-rank and dense transform to the pre-trained weight. This learnable transform is expected to align the pre-trained weight as closely as possible to the desired weight, thereby reducing the rank of the residual weight. Then, the residual part can be effectively approximated by more compact and parameter-efficient structures, with a smaller approximation error. To achieve ultra-parameter-efficiency in practice, we design highly flexible and effective tensor decompositions for both the transform and residual adaptations. Additionally, popular PEFT methods such as DoRA can be summarized under this transform plus residual adaptation scheme. Experiments are conducted on fine-tuning Stable Diffusion models in subject-driven and controllable generation. The results manifest that our method can achieve better performances and parameter efficiency compared to LoRA and several baselines.