GenAI4ELab/papercli-papers huggingface dataset:
Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
Authors: Sunghyun Park; Jungsoo Lee; Shubhankar  Borse; Munawar Hayat; Sungha Choi; Kyuwoong Hwang; Fatih Porikli
Venue: ICCV 2025 (main)

Abstract: While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training, it fails to understand personal texts (e.g. 'my mug cup') for segmenting regions of specific interest to users. This paper addresses challenges like recognizing 'my mug cup' among 'multiple mug cups'. To overcome this challenge, we introduce a novel task termed personalized open-vocabulary semantic segmentation and propose a text prompt tuning-based plug-in method designed to recognize personal visual concepts using a few pairs of images and masks, while maintaining the performance of the original OVSS. Based on the observation that reducing false predictions is essential when applying text prompt tuning to this task, our proposed method employs 'negative mask proposal' that captures visual concepts other than the personalized concept. We further improve the performance by enriching the representation of text prompts by injecting visual embeddings of the personal concept into them. This approach enhances personalized OVSS without compromising the original OVSS performance. We demonstrate the superiority of our method on our newly established benchmarks for this task, including FSS^per, CUB^per, and ADE^per.
4D Visual Pre-training for Robot Learning
Authors: Chengkai Hou; Yanjie Ze; Yankai Fu; Zeyu Gao; Songbo Hu; Yue Yu; Shanghang Zhang; Huazhe Xu
Venue: ICCV 2025 (main)

Abstract: General visual representations learned from web-scale datasets for robotics have achieved great success in recent years, enabling data-efficient robot learning on manipulation tasks; yet these pre-trained representations are mostly on 2D images, neglecting the inherent 3D nature of the world. However, due to the scarcity of large-scale 3D data, it is still hard to extract a universal 3D representation from web datasets. Instead, we are seeking a general visual pre-training framework that could improve all 3D representations as an alternative. Our framework, called FVP, is a novel 4D Visual Pre-training framework for real-world robot learning. FVP frames the visual pre-training objective as a next-point-cloud-prediction problem, models the prediction model as a diffusion model, and pre-trains the model on the larger public datasets directly. Across twelve real-world manipulation tasks, FVP boosts the average success rate of 3D Diffusion Policy (DP3) for these tasks by 28%. The FVP pre-trained DP3 achieves state-of-the-art performance across imitation learning methods. Moreover, the efficacy of \ours adapts across various point cloud encoders and datasets. Finally, we apply FVP to the RDT-1B, a larger Vision-Language-Action robotic model, enhancing its performance on various robot tasks. Our project page is available at: https://4d-visual-pretraining.github.io/.
SemTalk: Holistic Co-speech Motion Generation with Frame-level Semantic Emphasis
Authors: Xiangyue Zhang; Jianfang Li; Jiaxu Zhang; Ziqiang Dang; Jianqiang Ren; Liefeng Bo; Zhigang Tu
Venue: ICCV 2025 (main)

Abstract: A good co-speech motion generation cannot be achieved without a careful integration of common rhythmic motion and rare yet essential semantic motion. In this work, we propose SemTalk for holistic co-speech motion generation with frame-level semantic emphasis. Our key insight is to separately learn base motions and sparse motions, and then adaptively fuse them. In particular, coarse2fine cross-attention module and rhythmic consistency learning are explored to establish rhythm-related base motion, ensuring a coherent foundation that synchronizes gestures with the speech rhythm. Subsequently, semantic emphasis learning is designed to generate semantic-aware sparse motion, focusing on frame-level semantic cues. Finally, to integrate sparse motion into the base motion and generate semantic-emphasized co-speech gestures, we further leverage a learned semantic score for adaptive synthesis. Qualitative and quantitative comparisons on two public datasets demonstrate that our method outperforms the state-of-the-art, delivering high-quality co-speech motion with enhanced semantic richness over a stable base motion.
Textured 3D Regenerative Morphing with 3D Diffusion Prior
Authors: Songlin Yang; Yushi Lan; Honghua Chen; Xingang Pan
Venue: ICCV 2025 (main)

Abstract: Textured 3D morphing creates smooth and plausible interpolation sequences between two 3D objects, focusing on transitions in both shape and texture. This is important for creative applications like visual effects in filmmaking. Previous methods rely on establishing point-to-point correspondences and determining smooth deformation trajectories, which inherently restrict them to shape-only morphing on untextured, topologically aligned datasets. This restriction leads to labor-intensive preprocessing and poor generalization. To overcome these challenges, we propose a method for 3D regenerative morphing using a 3D diffusion prior. Unlike previous methods that depend on explicit correspondences and deformations, our method eliminates the additional need for obtaining correspondence and uses the 3D diffusion prior to generate morphing. Specifically, we first introduce a 3D diffusion model and interpolate the source and target information at three levels: initial noise, model parameters, and condition features. We then explore an Attention Fusion strategy to generate smoother morphing sequences. To further improve the plausibility of semantic interpolation and the generated 3D surfaces, we propose two strategies: (a) Token Reordering, where we match approximate tokens based on semantic analysis to guide implicit correspondences in the denoising process of the diffusion model, and (b) Low-Frequency Enhancement, where we enhance low-frequency signals in the tokens to improve the quality of generated surfaces. Experimental results show that our method achieves superior smoothness and plausibility in 3D morphing across diverse cross-category object pairs, offering a novel regenerative method for 3D morphing with textured representations.
LONG3R: Long Sequence Streaming 3D Reconstruction
Authors: Zhuoguang Chen; Minghui Qin; Tianyuan Yuan; Zhe Liu; Hang Zhao
Venue: ICCV 2025 (main)

Abstract: Recent advancements in multi-view scene reconstruction have been significant, yet existing methods face limitations when processing streams of input images. These methods either rely on time-consuming offline optimization or are restricted to shorter sequences, hindering their applicability in real-time scenarios. In this work, we propose LONG3R (LOng sequence streamiNG 3D Reconstruction), a novel model designed for streaming multi-view 3D scene reconstruction over longer sequences. Our model achieves real-time processing by operating recurrently, maintaining and updating memory with each new observation. We first employ a memory gating mechanism to filter relevant memory, which, together with a new observation, is fed into a dual-source refined decoder for coarse-to-fine interaction. To effectively capture long-sequence memory, we propose a 3D spatio-temporal memory that dynamically prunes redundant spatial information while adaptively adjusting resolution along the scene. To enhance our model's performance on long sequences while maintaining training efficiency, we employ a two-stage curriculum training strategy, each stage targeting specific capabilities. Experiments demonstrate that LONG3R outperforms state-of-the-art streaming methods, particularly for longer sequences, while maintaining real-time inference speed. Project page: https://zgchen33.github.io/LONG3R/.
DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance
Authors: Yuxuan Luo; Zhengkun Rong; Lizhen Wang; Longhao Zhang; Tianshu Hu
Venue: ICCV 2025 (main)

Abstract: While recent image-based human animation methods achieve realistic body and facial motion synthesis, critical gaps remain in fine-grained holistic controllability, multi-scale adaptability, and long-term temporal coherence, which leads to their lower expressiveness and robustness. We propose a diffusion transformer (DiT) based framework, HERA, with hybrid guidance to overcome these limitations. For motion guidance, our hybrid control signals that integrate implicit facial representations, 3D head spheres, and 3D body skeletons achieve robust control of facial expressions and body movements, while producing expressive and identity-preserving animations.For scale adaptation, to handle various body poses and image scales ranging from portraits to full-body views, we employ a progressive training strategy using data with varying resolutions and scales.For appearance guidance, we integrate motion patterns from sequential frames with complementary visual references, ensuring long-term temporal coherence for unseen regions during complex movements.Experiments demonstrate that our method outperforms the state-of-the-art works, delivering expressive results for portraits, upper-body, and full-body generation with robust long-term consistency.
Blind2Sound: Self-Supervised Image Denoising without Residual Noise
Authors: Jiazheng Liu; Zejin Wang; Bohao Chen; Hua Han
Venue: ICCV 2025 (main)

Abstract: Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visible loss senses noise levels and performs personalized denoising without noise residues while retaining the signal lossless. The theoretical analysis of intermediate medium gradients guarantees stable training, while the Cramer Gaussian loss acts as a regularization to facilitate the accurate perception of noise levels and improve the performance of the denoiser. Experiments on synthetic and real-world datasets show the superior performance of our method, especially for single-channel images. The code is publicly available from this link.
Sparsity Outperforms Low-Rank Projections in Few-Shot Adaptation
Authors: Nairouz Mrabah; Nicolas Richet; Ismail Ben Ayed; Eric Granger
Venue: ICCV 2025 (main)

Abstract: Adapting Vision-Language Models (VLMs) to new domains with few labeled samples remains a significant challenge due to severe overfitting and computational constraints. State-of-the-art solutions, such as low-rank reparameterization, mitigate these issues but often struggle with generalization and require extensive hyperparameter tuning. In this paper, a novel Sparse Optimization (SO) framework is proposed. Unlike low-rank approaches that typically constrain updates to a fixed subspace, our SO method leverages high sparsity to dynamically adjust very few parameters. We introduce two key paradigms. First, we advocate for local sparsity and global density, which updates a minimal subset of parameters per iteration while maintaining overall model expressiveness. As a second paradigm, we advocate for local randomness and global importance, which sparsifies the gradient using random selection while pruning the first moment based on importance. This combination significantly mitigates overfitting and ensures stable adaptation in low-data regimes. Extensive experiments on 11 diverse datasets show that SO achieves state-of-the-art few-shot adaptation performance while reducing memory overhead.
Hyper-Depth: Hypergraph-based Multi-Scale Representation Fusion for Monocular Depth Estimation
Authors: Lin Bie; Siqi Li; Yifan Feng; Yue Gao
Venue: ICCV 2025 (main)

Abstract: Monocular depth estimation (MDE) is a fundamental problem in computer vision with wide-ranging applications in various downstream tasks. While multi-scale features are perceptually critical for MDE, existing transformer-based methods have yet to leverage them explicitly. To address this limitation, we propose a hypergraph-based multi-scale representation fusion framework, Hyper-Depth.The proposed Hyper-Depth incorporates two key components: a Semantic Consistency Enhancement (SCE) module and a Geometric Consistency Constraint (GCC) module. The SCE module, designed based on hypergraph convolution, aggregates global information and enhances the representation of multi-scale patch features. Meanwhile, the GCC module provides geometric guidance to reduce over-fitting errors caused by excessive reliance on local features. In addition, we introduce a Correlation-based Conditional Random Fields (C-CRFs) module as the decoder to filter correlated patches and compute attention weights more effectively.Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches across all evaluation metrics on the KITTI and NYU-Depth-v2 datasets, achieving improvements of 6.21% and 3.32% on the main metric RMSE, respectively. Furthermore, zero-shot evaluations on the nuScenes and SUN-RGBD datasets validate the generalizability of our approach.
PVMamba: Parallelizing Vision Mamba via Dynamic State Aggregation
Authors: Fei Xie; Zhongdao Wang; Weijia Zhang; Chao Ma
Venue: ICCV 2025 (main)

Abstract: Mamba, an architecture with RNN-like sequence modeling of State Space Model (SSM), has demonstrated promising capabilities in long-range modeling with high efficiency. However, Mamba models struggle with structured 2D visual data using sequential computing, thereby lagging behind their attention-based counterparts. In this paper, we propose a Parallel Vision Mamba (PVMamba), a novel SSM architecture tailored for visual data. PVMamba encompasses two key designs: 1) Based on the sparsity and adjacency of visual signals, we parallelize the sequential computing through three core steps, termed Dynamic State Aggregation (DSA), i.e., parallelization, alignment, and aggregation. DSA generates the hidden state in SSM by a feasible spatial aggregation, thereby overcoming the inherent sequential constraints. 2) Along with maintaining linear computational complexity, we apply a dynamic operator to learn the spatial samplings for each hidden state. To further boost the local modeling capability, we restrict the dynamic operator to the neighboring pixels in shallow layers. We also devise a layer multiplexing technique to stabilize the training and reduce the learning redundancy. PVMamba is a versatile backbone network with dynamic operators for various vision tasks, such as image classification and dense prediction. Extensive experiments show that PVMamba achieves state-of-the-art performance on a range of benchmarks.
Fuzzy Contrastive Decoding to Alleviate Object Hallucination in Large Vision-Language Models
Authors: Jieun Kim; Jinmyeong Kim; Yoonji Kim; Sung-Bae Cho
Venue: ICCV 2025 (main)

Abstract: Large vision-language models (LVLMs) often exhibit object hallucination, a phenomenon where models generate descriptions of non-existent objects within images. Prior methods have sought to mitigate this issue by adjusting model logits to reduce linguistic bias, but they often lack precise control over visual uncertainty, sometimes exacerbating hallucinations instead of mitigating them. To address this limitation, we propose a novel decoding strategy called fuzzy contrastive decoding (FuzzyCD) that uses Takagi-Sugeno fuzzy inference to refine hallucination control. FuzzyCD adaptively assigns weights to high-hallucination logits while mitigating unnecessary linguistic bias. Specifically, it transforms the log-probabilities of top-1 tokens from both standard and hallucination logits into a confidence linguistic fuzzy set. Through Takagi-Sugeno fuzzy inference, it dynamically adjusts hallucination logits to prevent the model from over-relying on spurious linguistic patterns. Experimental results on object hallucination datasets demonstrate that hallucination is mitigated by 11%p compared to conventional LVLMs. In-depth analyses highlight the effectiveness of FuzzyCD in enhancing the reliability of vision-language models.
SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images
Authors: Gencer Sumbul; Chang Xu; Emanuele Dalsasso; Devis Tuia
Venue: ICCV 2025 (main)

Abstract: From optical sensors to microwave radars, leveraging the complementary strengths of remote sensing (RS) sensors is crucial for achieving dense spatio-temporal monitoring of our planet. In contrast, recent deep learning models, whether task-specific or foundational, are often specific to single sensors or to fixed combinations: adapting such models to different sensory inputs requires both architectural changes and re-training, limiting scalability and generalization across multiple RS sensors. On the contrary, a single model able to modulate its feature representations to accept diverse sensors as input would pave the way to agile and flexible multi-sensor RS data processing. To address this, we introduce SMARTIES, a generic and versatile foundation model lifting sensor-specific/dependent efforts and enabling scalability and generalization to diverse RS sensors: SMARTIES projects data from heterogeneous sensors into a shared spectrum-aware space, enabling the use of arbitrary combinations of bands both for training and inference. To obtain sensor-agnostic representations, we train a single, unified transformer model reconstructing masked multi-sensor data with cross-sensor token mixup. On both single- and multi-modal tasks across diverse sensors, SMARTIES outperforms previous models that rely on sensor-specific pretraining. Our code and pretrained models are available at https://gsumbul.github.io/SMARTIES.
SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
Authors: Xilin He; Cheng Luo; Xiaole Xian; Bing Li; Muhammad Haris Khan; Zongyuan Ge; Weicheng Xie; Siyang Song; Linlin Shen; Bernard Ghanem; Xiangyu Yue
Venue: ICCV 2025 (main)

Abstract: Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection. This limitation poses a significant challenge for developing modern deep learning-based facial expression analysis models, particularly foundation models, that rely on large-scale data for optimal performance. To tackle the overarching and complex challenge, we introduce SynFER (Synthesis of Facial Expressions with Refined Control), a novel framework for synthesizing facial expression image data based on high-level textual descriptions as well as more fine-grained and precise control through facial action units. To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique to steer the generation process and a pseudo-label generator to help rectify the facial expression labels for the synthetic images. To demonstrate the generation fidelity and the effectiveness of the synthetic data from SynFER, we conduct extensive experiments on representation learning using both synthetic data and real-world data. Results validate the efficacy of our approach and the synthetic data. Notably, our approach achieves a 67.23% classification accuracy on AffectNet when training solely with synthetic data equivalent to the AffectNet training set size, which increases to 69.84% when scaling up to five times the original size.
Boosting Multimodal Learning via Disentangled Gradient Learning
Authors: Shicai Wei; Chunbo Luo; Yang Luo
Venue: ICCV 2025 (main)

Abstract: Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL.
Generating Physically Stable and Buildable Brick Structures from Text
Authors: Ava Pun; Kangle Deng; Ruixuan Liu; Deva Ramanan; Changliu Liu; Jun-Yan Zhu
Venue: ICCV 2025 (main)

Abstract: We introduce BrickGPT, the first approach for generating physically stable interconnecting brick assembly models from text prompts. To achieve this, we construct a large-scale, physically stable dataset of brick structures, along with their associated captions, and train an autoregressive large language model to predict the next brick to add via next-token prediction. To improve the stability of the resulting designs, we employ an efficient validity check and physics-aware rollback during autoregressive inference, which prunes infeasible token predictions using physics laws and assembly constraints. Our experiments show that BrickGPT produces stable, diverse, and aesthetically pleasing brick structures that align closely with the input text prompts. We also develop a text-based brick texturing method to generate colored and textured designs. We show that our designs can be assembled manually by humans and automatically by robotic arms. We release our new dataset, StableText2Brick, containing over 47,000 brick structures of over 28,000 unique 3D objects accompanied by detailed captions, along with our code and models at the project website: https://avalovelace1.github.io/BrickGPT/.