GenAI4ELab/papercli-papers huggingface dataset:
Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts
Authors: Zixuan Hu; Dongxiao Li; Xinzhu Ma; Shixiang Tang; Xiaotong Li; Wenhan Yang; Ling-Yu Duan
Venue: ICCV 2025 (main)

Abstract: Accurate monocular 3D object detection (M3OD) is pivotal for safety-critical applications like autonomous driving, yet its reliability deteriorates significantly under real-world domain shifts caused by environmental or sensor variations. To address these shifts, Test-Time Adaptation (TTA) methods have emerged, enabling models to adapt to target distributions during inference. While prior TTA approaches recognize the positive correlation between low uncertainty and high generalization ability, they fail to address the dual uncertainty inherent to M3OD: semantic uncertainty (ambiguous class predictions) and geometric uncertainty (unstable spatial localization). To bridge this gap, we propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD. Through a convex optimization lens, we introduce an innovative convex structure of the focal loss and further derive a novel conjugate loss, enabling label-agnostic uncertainty weighting and balanced learning for high-uncertainty objects. In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues, reducing uncertainty from the unstable 3D representation. This dual-branch mechanism forms a complementary loop: enhanced spatial perception improves semantic classification, and robust semantic predictions further refine spatial understanding. Extensive experiments demonstrate the superiority of DUO over existing methods across various datasets and domain shift types. The source code is available at https://github.com/hzcar/DUO.
RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
Authors: Junwen  Huang; Shishir Reddy Vutukur; Peter KT Yu; Nassir Navab; Slobodan Ilic; Benjamin Busam
Venue: ICCV 2025 (main)

Abstract: Typical template-based object pose pipelines estimate the pose by retrieving the closest matching template and aligning it with the observed image. However, failure to retrieve the correct template often leads to inaccurate pose predictions. To address this, we reformulate template-based object pose estimation as a ray alignment problem, where the viewing directions from multiple posed template images are learned to align with a non-posed query image. Inspired by recent progress in diffusion-based camera pose estimation, we embed this formulation into a diffusion transformer architecture that aligns a query image with a set of posed templates. We reparameterize object rotation using object-centered camera rays and model object translation by extending scale-invariant translation estimation to dense translation offsets. Our model leverages geometric priors from the templates to guide accurate query pose inference. A coarse-to-fine training strategy based on narrowed template sampling improves performance without modifying the network architecture. Extensive experiments across multiple benchmark datasets show competitive results of our method compared to state-of-the-art approaches in unseen object pose estimation.
DuoLoRA : Cycle-consistent and Rank-disentangled Content-Style Personalization
Authors: Aniket Roy; Shubhankar Borse; Shreya Kadambi; Debasmit Das; Shweta Mahajan; Risheek Garrepalli; Hyojin Park; Ankita Nayak; Rama Chellappa; Munawar Hayat; Fatih Porikli
Venue: ICCV 2025 (main)

Abstract: We tackle the challenge of jointly personalizing content and style from a few examples. A promising approach is to train separate Low-Rank Adapters (LoRA) and merge them effectively, preserving both content and style. Existing methods, such as ZipLoRA, treat content and style as independent entities, merging them by learning masks in LoRA's output dimensions. However, content and style are intertwined, not independent. To address this, we propose DuoLoRA--a content-style personalization framework featuring three key components: (1) rank-dimension mask learning, (2) effective merging via layer priors, and (3) Constyle loss, which leverages cycle-consistency in the merging process.First, we introduce ZipRank, which performs content-style merging within the rank dimension, offering adaptive rank flexibility and significantly reducing the number of learnable parameters. Additionally, we incorporate SDXL layer priors to apply implicit rank constraints informed by each layer's content-style bias and adaptive merger initialization, enhancing the integration of content and style. To further refine the merging process, we introduce Constyle loss, which leverages the cycle consistency between content and style.Our experimental results demonstrate that DuoLoRA outperforms state-of-the-art content-style merging methods across multiple benchmarks.
F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration
Authors: Lu Liu; Huiyu Duan; Qiang Hu; Liu Yang; Chunlei Cai; Tianxiao Ye; Huayu Liu; Xiaoyun Zhang; Guangtao Zhai
Venue: ICCV 2025 (main)

Abstract: Recent artificial intelligence (AI) generative models have demonstrated remarkable capabilities in image production, and have been widely applied to face image generation, customization, and restoration. However, many AI-generated faces (AIGFs) still suffer from issues such as unique distortions, unrealistic details, and unexpected identity shifts, underscoring the need for a comprehensive quality evaluation method for AIGFs. To this end, we introduce **FaceQ**, the first comprehensive AI-generated Face image database with fine-grained Quality annotations aligned with human preferences, which consists of 12K images and 491K ratings across multiple dimensions. Using the FaceQ database, we establish **F-Bench**, a benchmark for comparing and evaluating face generation, customization, and restoration models, highlighting strengths and weaknesses across various prompts and evaluation dimensions. Additionally, we assess the performance of existing image quality assessment (IQA) methods on FaceQ, and further propose a large multimodal model (LMM) based Face quality Evaluator (**F-Eval**) to accurately assess the multi-dimensional quality of generated faces in a one-for-all manner. Extensive experimental results demonstrate the state-of-the-art performance of our F-Eval.
MikuDance: Animating Character Art with Mixed Motion Dynamics
Authors: Jiaxu Zhang; Xianfang Zeng; Xin Chen; Wei Zuo; Gang Yu; Zhigang Tu
Venue: ICCV 2025 (main)

Abstract: We propose MikuDance, a diffusion-based pipeline incorporating mixed motion dynamics to animate stylized character art. MikuDance consists of two key techniques: Mixed Motion Modeling and Mixed-Control Diffusion, to address the challenges of high-dynamic motion and reference-guidance misalignment in character art animation. Specifically, a Scene Motion Tracking strategy is presented to explicitly model the dynamic camera in pixel-wise space, enabling unified character-scene motion modeling. Building on this, the Mixed-Control Diffusion implicitly aligns the scale and body shape of diverse characters with motion guidance, allowing flexible control of local character motion. Subsequently, a Motion-Adaptive Normalization module is incorporated to effectively inject global scene motion, paving the way for comprehensive character art animation. Through extensive experiments, we demonstrate the effectiveness and generalizability of MikuDance across various character art and motion guidance, consistently producing high-quality animations with remarkable motion dynamics.
GaussRender: Learning 3D Occupancy with Gaussian Rendering
Authors: Loick  Chambon; Eloi Zablocki; Alexandre  Boulch; Mickael Chen; Matthieu Cord
Venue: ICCV 2025 (main)

Abstract: Understanding the 3D geometry and semantics of driving scenes is critical for developing safe autonomous vehicles. Recent advances in 3D occupancy prediction have improved scene representation but often suffer from spatial inconsistencies, leading to floating artifacts and poor surface localization. Existing voxel-wise losses (e.g., cross-entropy) fail to enforce geometric coherence. In this paper, we propose GaussRender, a module that improves 3D occupancy learning by enforcing projective consistency. Our key idea is to project both predicted and ground-truth 3D occupancy into 2D camera views, where we apply supervision. Our method penalizes 3D configurations that produce inconsistent 2D projections, thereby enforcing a more coherent and geometrically plausible 3D structure. To achieve this efficiently, we leverage differentiable rendering with Gaussian splatting. GaussRender seamlessly integrates with existing architectures while maintaining efficiency and requiring no inference-time modifications. Extensive evaluations on multiple benchmarks (SurroundOcc-nuScenes, Occ3D nuScenes, SSCBench-KITTI360) demonstrate that GaussRender significantly improves geometric fidelity across various 3D occupancy models (TPVFormer, SurroundOcc, Symphonies), achieving state-of-the-art results, particularly on surface-sensitive metrics. The code and models will be open-sourced.
UniDxMD: Towards Unified Representation for Cross-Modal Unsupervised Domain Adaptation in 3D Semantic Segmentation
Authors: Zhengyin Liang; Hui Yin; Min Liang; Qianqian Du; Ying Yang; Hua Huang
Venue: ICCV 2025 (main)

Abstract: Modality or domain distribution shifts pose formidable challenges in 3D semantic segmentation. Existing methods predominantly address either cross-modal or cross-domain adaptation in isolation, leading to insufficient exploration of semantic associations and complementary features in heterogeneous data. To bridge this gap, we present UniDxMD, a unified representation method for cross-modal unsupervised domain adaptation (UDA) in 3D semantic segmentation that simultaneously tackles both cross-modal and cross-domain adaptation objectives. Our core insight is deriving a unified discrete representation from heterogeneous data to mitigate distribution shifts, inspired by vector quantization. Specifically, we propose a differentiable, cluster-based soft quantization mechanism (CSQM) that maps heterogeneous data (spanning modalities and domains) into a shared discrete latent space. Then, we introduce latent space regularization (LSR), leveraging joint prototypes that satisfy semantic relation consistency as learnable anchors to enhance the compactness and semantic discriminability of the discrete latent space. Our method paves the way for advancing cross-modal UDA in 3D semantic segmentation towards the unified representation. Extensive results across four challenging cross-modal UDA scenarios demonstrate the superiority of our method.
GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing
Authors: Tianyang Xue; Lin Lu; Yang Liu; Mingdong Wu; Hao Dong; Yanbin Zhang; Renmin Han; Baoquan Chen
Venue: ICCV 2025 (main)

Abstract: 2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. Due to its NP-hard nature, conventional numerical approaches typically encounter slow convergence and high computational costs. Previous research GFPack introduced a generative method for gradient-based packing, providing early evidence of its feasibility but faced limitations such as insufficient rotation support, poor boundary adaptability, and high overlap ratios. In this paper, we propose GFPack++, a deeply investigated framework that adopts attention-based geometry and relation encoding, enabling more comprehensive modeling of complex packing relationships. We further design a constrained gradient and a weighting function to enhance both the feasibility of the produced solutions and the learning effectiveness. Experimental results on multiple datasets demonstrate that GFPack++ achieves higher space utilization, supports continuous rotation, generalizes well to arbitrary boundaries, and infers orders of magnitude faster than previous approaches. Codes for this paper are at https://github.com/TimHsue/GFPack-pp.
PLA: Prompt Learning Attack against Text-to-Image Generative Models
Authors: Xinqi Lyu; Yihao Liu; Yanjie Li; Bin Xiao
Venue: ICCV 2025 (main)

Abstract: Text-to-Image (T2I) models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work (NSFW) content. To investigate the vulnerability of T2I models, this paper delves into adversarial attacks to bypass the safety mechanisms under black-box settings. Most previous methods rely on word substitution to search adversarial prompts. Due to limited search space, this leads to suboptimal performance compared to gradient-based training. However, black-box settings present unique challenges to training gradient-driven attack methods, since there is no access to the internal architecture and parameters of T2I models. To facilitate the learning of adversarial prompts in black-box settings, we propose a novel prompt learning attack framework (PLA), where insightful gradient-based training tailored to black-box T2I models is designed by utilizing multimodal similarities. Experiments show that our new method can effectively attack the safety mechanisms of black-box T2I models including prompt filters and post-hoc safety checkers with a high success rate compared to state-of-the-art methods. Warning: This paper may contain offensive model-generated content.
HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis
Authors: Timo Teufel; Pulkit Gera; Xilong Zhou; Umar Iqbal; Pramod Rao; Jan Kautz; Vladislav Golyanik; Christian Theobalt
Venue: ICCV 2025 (main)

Abstract: Simultaneous relighting and novel-view rendering of digital human representations is an important yet challenging task with numerous applications. However, progress in this area has been significantly limited due to the lack of publicly available, high-quality datasets, especially for full-body human captures. To address this critical gap, we introduce the HumanOLAT dataset, the first publicly accessible large-scale dataset providing multi-view One-Light-at-a-Time (OLAT) captures of full-body humans. The dataset includes HDR RGB frames under various illumination conditions, such as white light, environment maps, color gradients and fine-grained OLAT illuminations. Our evaluations on state-of-the-art relighting and novel-view synthesis methods underscore both the dataset's value and the significant challenges still present in accurately modeling complex human-centric appearance and lighting interactions. We believe that HumanOLAT will significantly facilitate future research, enabling rigorous benchmarking and advancements in both general and human-specific relighting and rendering techniques.
Integrating Task-Specific and Universal Adapters for Pre-Trained Model-based Class-Incremental Learning
Authors: Yan Wang; Da-Wei Zhou; Han-Jia Ye
Venue: ICCV 2025 (main)

Abstract: Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Existing pre-trained model-based CIL methods often freeze the pre-trained network and adapt to incremental tasks using additional lightweight modules such as adapters. However, incorrect module selection during inference hurts performance, and task-specific modules often overlook shared general knowledge, leading to errors on distinguishing between similar classes across tasks. To address the aforementioned challenges, we propose integrating Task-Specific and Universal Adapters (TUNA) in this paper. Specifically, we train task-specific adapters to capture the most crucial features relevant to their respective tasks and introduce an entropy-based selection mechanism to choose the most suitable adapter. Furthermore, we leverage an adapter fusion strategy to construct a universal adapter, which encodes the most discriminative features shared across tasks. We combine task-specific and universal adapter predictions to harness both specialized and general knowledge during inference. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of our approach. Code is available at https://github.com/LAMDA-CL/ICCV2025-TUNA
RoboFactory: Exploring Embodied Agent Collaboration with Compositional Constraints
Authors: Yiran Qin; Li Kang; Xiufeng Song; Zhenfei Yin; Xiaohong Liu; Xihui Liu; Ruimao Zhang; Lei Bai
Venue: ICCV 2025 (main)

Abstract: Designing effective embodied multi-agent systems is critical for solving complex real-world tasks across domains. Due to the complexity of multi-agent embodied systems, existing methods fail to automatically generate safe and efficient training data for such systems. To this end, we propose the concept of compositional constraints for embodied multi-agent systems, addressing the challenges arising from collaboration among embodied agents. We design various interfaces tailored to different types of constraints, enabling seamless interaction with the physical world. Leveraging compositional constraints and specifically designed interfaces, we develop an automated data collection framework for embodied multi-agent systems and introduce the first benchmark for embodied multi-agent manipulation, RoboFactory. Based on RoboFactory benchmark, we adapt and evaluate the method of imitation learning and analyzed its performance in different difficulty agent tasks. Furthermore, we explore the architectures and training strategies for multi-agent imitation learning, aiming to build safe and efficient embodied multi-agent systems.
Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction
Authors: Jixuan Fan; Wanhua Li; Yifei Han; Tianru Dai; Yansong Tang
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting has demonstrated notable success in large-scale scene reconstruction, but challenges persist due to high training memory consumption and storage overhead. Hybrid representations that integrate implicit and explicit features offer a way to mitigate these limitations. However, when applied in parallelized block-wise training, two critical issues arise since reconstruction accuracy deteriorates due to reduced data diversity when training each block independently, and parallel training restricts the number of divided blocks to the available number of GPUs. To address these issues, we propose Momentum-GS, a novel approach that leverages momentum-based self-distillation to promote consistency and accuracy across the blocks while decoupling the number of blocks from the physical GPU count. Our method maintains a teacher Gaussian decoder updated with momentum, ensuring a stable reference during training. This teacher provides each block with global guidance in a self-distillation manner, promoting spatial consistency in reconstruction. To further ensure consistency across the blocks, we incorporate block weighting, dynamically adjusting each block's weight according to its reconstruction accuracy. Extensive experiments on large-scale scenes show that our method consistently outperforms existing techniques, achieving a 18.7% improvement in LPIPS over CityGaussian with much fewer divided blocks and establishing a new state of the art.
SEHDR: Single-Exposure HDR Novel View Synthesis via 3D Gaussian Bracketing
Authors: Yiyu Li; Haoyuan Wang; Ke Xu; Gerhard Petrus Hancke; Rynson W.H. Lau
Venue: ICCV 2025 (main)

Abstract: This paper presents SeHDR, a novel high dynamic range 3D Gaussian Splatting (HDR-3DGS) approach for generating HDR novel views given multi-view LDR images. Unlike existing methods that typically require the multi-view LDR input images to be captured from different exposures, which are tedious to capture and more likely to suffer from errors (e.g., object motion blurs and calibration/alignment inaccuracies), our approach learns the HDR scene representation from multi-view LDR images of a single exposure. Our key insight to this ill-posed problem is that by first estimating **Bracketed 3D Gaussians** (i.e., with different exposures) from single-exposure multi-view LDR images, we may then be able to merge these bracketed 3D Gaussians into an HDR scene representation. Specifically, SeHDR first learns base 3D Gaussians from single-exposure LDR inputs, where the spherical harmonics parameterize colors in a linear color space. We then estimate multiple 3D Gaussians with identical geometry but varying linear colors conditioned on exposure manipulations. Finally, we propose the Differentiable Neural Exposure Fusion (NeEF) to integrate the base and estimated 3D Gaussians into HDR Gaussians for novel view rendering. Extensive experiments demonstrate that SeHDR outperforms existing methods as well as carefully designed baselines.
TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions
Authors: Ilya A. Petrov; Riccardo Marin; Julian Chibane; Gerard Pons-Moll
Venue: ICCV 2025 (main)

Abstract: Modeling 3D human-object interaction (HOI) is a problem of great interest for computer vision and a key enabler for virtual and mixed-reality applications. Existing methods work in a one-way direction: some recover plausible human interactions conditioned on a 3D object; others recover the object pose conditioned on a human pose. Instead, we provide the first unified model - TriDi which works in any direction. Concretely, we generate Human, Object, and Interaction modalities simultaneously with a new three-way diffusion process, allowing to model seven distributions with one network. We implement TriDi as a transformer attending to the various modalities' tokens, thereby discovering conditional relations between them. The user can control the interaction either as a text description of HOI or a contact map. We embed these two representations onto a shared latent space, combining the practicality of text descriptions with the expressiveness of contact maps. Using a single network, TriDi unifies all the special cases of prior work and extends to new ones modeling a family of seven distributions. Remarkably, despite using a single model, TriDi generated samples surpass one-way specialized baselines on GRAB and BEHAVE in terms of both qualitative and quantitative metrics, and demonstrating better diversity. We show applicability of TriDi to scene population, generating object for human-contact datasets, and generalization to unseen object geometry.