GenAI4ELab/papercli-papers huggingface dataset:
Scaling Laws for Native Multimodal Models
Authors: Mustafa Shukor; Enrico Fini; Victor Guilherme Turrisi da Costa; Matthieu Cord; Joshua Susskind; Alaaeldin El-Nouby
Venue: ICCV 2025 (main)

Abstract: Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs)--those trained from the ground up on all modalities--and conduct an extensive scaling laws study, spanning 457 trained models with different architectures and training mixtures. Our investigation reveals no inherent advantage to late-fusion architectures over early-fusion ones, which do not rely on image encoders or tokenizers. On the contrary, early fusion exhibits stronger performance at lower parameter counts, is more efficient to train, and is easier to deploy. Motivated by the strong performance of the early-fusion architectures, we show that incorporating Mixture of Experts (MoEs) allows models to learn modality-specific weights, significantly benefiting performance.
Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework
Authors: Yi-Ting Chen; Ting-Hsuan Liao; Pengsheng Guo; Alexander  Schwing; Jia-Bin Huang
Venue: ICCV 2025 (main)

Abstract: We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released.
MagicID: Hybrid Preference Optimization for ID-Consistent and Dynamic-Preserved Video Customization
Authors: Hengjia Li; Lifan Jiang; Xi Xiao; Tianyang Wang; Hongwei Yi; Boxi Wu; Deng Cai
Venue: ICCV 2025 (main)

Abstract: Video identity customization seeks to produce high-fidelity videos that maintain consistent identity and exhibit significant dynamics based on users' reference images. However, existing approaches face two key challenges: identity degradation over extended video length and reduced dynamics during training, primarily due to their reliance on traditional self-reconstruction training with static images. To address these issues, we introduce MagicID, a novel framework designed to directly promote the generation of identity-consistent and dynamically rich videos tailored to user preferences. Specifically, we propose constructing pairwise preference video data with explicit identity and dynamic rewards for preference learning, instead of sticking to the traditional self-reconstruction. To address the constraints of customized preference data, we introduce a hybrid sampling strategy. This approach first prioritizes identity preservation by leveraging static videos derived from reference images, then enhances dynamic motion quality in the generated videos using a Frontier-based sampling method. By utilizing these hybrid preference pairs, we optimize the model to align with the reward differences between pairs of customized preferences. Extensive experiments show that MagicID successfully achieves consistent identity and natural dynamics, surpassing existing methods across various metrics.
LookOut: Real-World Humanoid Egocentric Navigation
Authors: Boxiao Pan; Adam W. Harley; Francis Engelmann; C. Karen Liu; Leonidas J. Guibas
Venue: ICCV 2025 (main)

Abstract: The ability to predict collision-free future trajectories from egocentric observations is crucial in applications such as humanoid robotics, VR / AR, and assistive navigation. In this work, we introduce the challenging problem of predicting a sequence of future 6D head poses from an egocentric video. In particular, we predict both head translations and rotations to learn the active information-gathering behavior expressed through head-turning events. To solve this task, we propose a framework that reasons over temporally aggregated 3D latent features, which models the geometric and semantic constraints for both the static and dynamic parts of the environment. Motivated by the lack of training data in this space, we further contribute a data collection pipeline using the Project Aria glasses, and present a dataset collected through this approach. Our dataset, dubbed Aria Navigation Dataset (AND), consists of 4 hours of recording of users navigating in real-world scenarios. It includes diverse situations and navigation behaviors, providing a valuable resource for learning real-world egocentric navigation policies. Extensive experiments show that our model learns human-like navigation behaviors such as waiting / slowing down, rerouting, and looking around for traffic while generalizing to unseen environments. Check out our project webpage at https://sites.google.com/stanford.edu/lookout.
Lightweight Gradient-Aware Upscaling of 3D Gaussian Splatting Images
Authors: Simon Niedermayr; Christoph Neuhauser; Rüdiger Westermann
Venue: ICCV 2025 (main)

Abstract: We introduce an image upscaling technique tailored for 3D Gaussian Splatting (3DGS) on lightweight GPUs. Compared to 3DGS, it achieves significantly higher rendering speeds and reduces artifacts commonly observed in 3DGS reconstructions. Our technique upscales low-resolution 3DGS renderings with a marginal increase in cost by directly leveraging the analytical image gradients of Gaussians for gradient-based bicubic spline interpolation.The technique is agnostic to the specific 3DGS implementation, achieving novel view synthesis at rates 3x-4x higher than the baseline implementation.Through extensive experiments on multiple datasets, we showcase the performance improvements and high reconstruction fidelity attainable with gradient-aware upscaling of 3DGS images.We further demonstrate the integration of gradient-aware upscaling into the gradient-based optimization of a 3DGS model and analyze its effects on reconstruction quality and performance.
Motion-2-to-3: Leveraging 2D Motion Data for 3D Motion Generations
Authors: Ruoxi Guo; Huaijin Pi; Zehong Shen; Qing Shuai; Zechen Hu; Zhumei Wang; Yajiao Dong; Ruizhen Hu; Taku Komura; Sida Peng; Xiaowei Zhou
Venue: ICCV 2025 (main)

Abstract: Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry.Existing methods often rely on 3D motion capture data, which requires special setups, resulting in high costs for data acquisition, ultimately limiting the diversity and scope of human motion. In contrast, 2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities.In this paper, we explore the use of 2D human motion extracted from videos as an alternative data source to improve text-driven 3D motion generation.Our approach introduces a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data.We first train a single-view 2D local motion generator on a large dataset of text-2D motion pairs.Then we fine-tune the generator with 3D data, transforming it into a multi-view generator that predicts view-consistent local joint motion and root dynamics.Evaluations on the well-acknowledged datasets and novel text prompts demonstrate that our method can efficiently utilizes 2D data, supporting a wider range of realistic 3D human motion generation.
Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation
Authors: Tianyu Zou; Shengwu Xiong; Ruilin Yao; Yi Rong
Venue: ICCV 2025 (main)

Abstract: This paper studies the few-shot segmentation (FSS) task, which aims to segment objects belonging to unseen categories in a query image by learning a model on a small number of well-annotated support samples. Our analysis of two mainstream FSS paradigms reveals that the predictions made by prototype learning methods are usually conservative, while those of affinity learning methods tend to be more aggressive. This observation motivates us to balance the conservative and aggressive information captured by these two types of FSS frameworks so as to improve the segmentation performance. To achieve this, we propose a Prototype-Affinity Hybrid Network (PAHNet), which introduces a Prototype-guided Feature Enhancement (PFE) module and an Attention Score Calibration (ASC) module in each attention block of an affinity learning model (called affinity learner). These two modules utilize the predictions generated by a pre-trained prototype learning model (called prototype predictor) to enhance the foreground information in support and query image representations and suppress the mismatched foreground-background (FG-BG) relationships between them, respectively. In this way, the aggressiveness of the affinity learner can be effectively mitigated, thereby eventually increasing the segmentation accuracy of our PAHNet method. Experimental results show that PAHNet outperforms most recently proposed methods across 1-shot and 5-shot settings on both PASCAL-5^i and COCO-20^i datasets, suggesting its effectiveness. The code is available at: https://github.com/tianyu-zou/PAHNet.
Large Scene Generation with Cube-Absorb Discrete Diffusion
Authors: Qianjiang Hu; Wei Hu
Venue: ICCV 2025 (main)

Abstract: Generating realistic 3D outdoor scenes is essential for applications in autonomous driving, virtual reality, environmental science, and urban development. Traditional 3D generation approaches using single-layer diffusion methods can produce detailed scenes for individual objects but struggle with high-resolution, large-scale outdoor environments due to scalability limitations. Recent hierarchical diffusion models tackle this by progressively scaling up low-resolution scenes. However, they often sample fine details from pure noise rather than from the coarse scene, which limits the efficiency. We propose a novel cube-absorb discrete diffusion (CADD) model, which employs low-resolution scenes as the base state in the diffusion process to generate fine details, eliminating the need to sample entirely from noise. Moreover, we introduce the Sparse Cube Diffusion Transformer (SCDT), a transformer-based model with a sparse cube attention operator, optimized for generating large-scale sparse voxel scenes. Our method demonstrates state-of-the-art performance on the CarlaSC and KITTI360 datasets, supported by qualitative visualizations and extensive ablation studies that highlight the impact of the CADD process and sparse cube attention operator on high-resolution 3D scene generation.
Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation
Authors: Yu Lei; Bingde Liu; Qingsong Xie; Haonan Lu; Zhijie Deng
Venue: ICCV 2025 (main)

Abstract: Text-to-3D generation based on score distillation of pre-trained 2D diffusion models has gained increasing interest, with variational score distillation (VSD) as a remarkable example. VSD proves that vanilla score distillation can be improved by introducing an extra score-based model, which characterizes the distribution of images rendered from 3D models, to correct the distillation gradient. Despite the theoretical foundations, VSD, in practice, is likely to suffer from slow and sometimes ill-posed convergence.In this paper, we perform an in-depth investigation of the interplay between the introduced score model and the 3D model, and find that there exists a mismatching problem between LoRA and 3D distributions in practical implementation. We can simply adjust their optimization order to improve the generation quality. By doing so, the score model looks ahead to the current 3D state and hence yields more reasonable corrections. Nevertheless, naive lookahead VSD may suffer from unstable training in practice due to the potential over-fitting. To address this, we propose to use a linearized variant of the model for score distillation, giving rise to the Linearized Lookahead Variational Score Distillation (L^2-VSD). L^2-VSD can be realized efficiently with forward-mode autodiff functionalities of existing deep learning libraries. Extensive experiments validate the efficacy of L^2-VSD, revealing its clear superiority over prior score distillation-based methods. We also show that our method can be seamlessly incorporated into any other VSD-based text-to-3D framework.
MS3D: High-Quality 3D Generation via Multi-Scale Representation Modeling
Authors: Guan Luo; Jianfeng Zhang
Venue: ICCV 2025 (main)

Abstract: High-quality textured mesh reconstruction from sparse-view images remains a fundamental challenge in computer graphics and computer vision. Traditional large reconstruction models operate in a single-scale manner, forcing the models to simultaneously capture global structure and local details, often resulting in compromised reconstructed shapes. In this work, we propose MS3D, a novel multi-scale 3D reconstruction framework. At its core, our method introduces a hierarchical structured latent representation for multi-scale modeling, coupled with a multi-scale feature extraction and integration mechanism. This enables progressive reconstruction, effectively decomposing the complex task of detailed geometry reconstruction into a sequence of easier steps. This coarse-to-fine approach effectively captures multi-frequency details, learns complex geometric patterns, and generalizes well across diverse objects while preserving fine-grained details. Extensive experiments demonstrate MS3D outperforms state-of-the-art methods and is broadly applicable to both image- and text-to-3D generation. The entire pipeline reconstructs high-quality textured meshes in under five seconds.
Memory-Efficient 4-bit Preconditioned Stochastic Optimization
Authors: Jingyang Li; Kuangyu Ding; Kim-Chuan Toh; Pan Zhou
Venue: ICCV 2025 (main)

Abstract: Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in large-scale neural network training. However, they incur substantial memory overhead due to the storage demands of non-diagonal preconditioning matrices. To address this, we introduce 4-bit quantization for Shampoo's preconditioners. We introduce two key methods: First, we apply Cholesky decomposition followed by quantization of the Cholesky factors, reducing memory usage by leveraging their lower triangular structure while better preserving spectral properties to minimize information loss. To our knowledge, this is the first quantization approach applied to Cholesky factors of preconditioners. Second, we incorporate error feedback in the quantization process, efficiently storing Cholesky factor and error state in the lower and upper triangular parts of the same matrix. Through extensive experiments, we demonstrate that combining Cholesky quantization with error feedback enhances memory efficiency and algorithm performance in large-scale deep-learning tasks. Theoretically, we also provide convergence proofs for quantized Shampoo under both smooth and non-smooth stochastic optimization settings.
On the Recovery of Cameras from Fundamental Matrices
Authors: Rakshith Madhavan; Federica Arrigoni
Venue: ICCV 2025 (main)

Abstract: The viewing graph is a compact tool to encode the geometry of multiple views: nodes represent uncalibrated cameras and edges represent fundamental matrices (when available). Most research focuses on theoretical analyses, exploring for which viewing graphs it is possible (in principle) to retrieve cameras from fundamental matrices, in the sense that the problem admits a unique solution for noiseless data. However, the practical task of recovering cameras from noisy fundamental matrices is still open, as available methods are limited to special graphs (such as those covered by triplets). In this paper, we develop the first method that can deal with the recovery of cameras from noisy fundamental matrices in a general viewing graph. Experimental results demonstrate the promise of the proposed approach on a variety of synthetic and real scenarios.
Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling Dynamic-Static Features
Authors: Liying Yang; Chen Liu; Zhenwei Zhu; Ajian Liu; Hui Ma; Jian Nong; Yanyan Liang
Venue: ICCV 2025 (main)

Abstract: Recently, the generation of dynamic 3D objects from a video has shown impressive results. Existing methods directly optimize Gaussians using whole information in frames. However, when dynamic regions are interwoven with static regions within frames, particularly if the static regions account for a large proportion, existing methods often overlook information in dynamic regions and are prone to overfitting on static regions. This leads to producing results with blurry textures. We consider that decoupling dynamic-static features to enhance dynamic representations can alleviate this issue. Thus, we propose a dynamic-static feature decoupling module (DSFD). Along temporal axes, it regards the regions of current frame features that possess significant differences relative to reference frame features as dynamic features. Conversely, the remaining parts are the static features. Then, we acquire decoupled features driven by dynamic features and current frame features. Moreover, to further enhance the dynamic representation of decoupled features from different viewpoints and ensure accurate motion prediction, we design a temporal-spatial similarity fusion module (TSSF). Along spatial axes, it adaptively selects similar information of dynamic regions. Hinging on the above, we construct a novel approach, DS4D. Experimental results verify our method achieves state-of-the-art (SOTA) results in video-to-4D. In addition, the experiments on a real-world scenario dataset demonstrate its effectiveness on the 4D scene. Project page: https://github.com/LiyingCV/DS4D.
FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
Authors: Huan Wang; Haoran Li; Huaming Chen; Jun Yan; Jiahua Shi; Jun Shen
Venue: ICCV 2025 (main)

Abstract: Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity issue, where biased data preferences across multiple clients, harming the model's convergence and performance. In this paper, we first introduce powerful diffusion models into the federated learning paradigm and show that diffusion representations are effective steers during federated training. To explore the possibility of using diffusion representations in handling data heterogeneity, we propose a novel diffusion-inspired Federated paradigm with Diffusion Representation Collaboration, termed FedDifRC, leveraging meaningful guidance of diffusion models to mitigate data heterogeneity. The key idea is to construct text-driven diffusion contrasting and noise-driven diffusion regularization, aiming to provide abundant class-related semantic information and consistent convergence signals. On the one hand, we exploit the conditional feedback from the diffusion model for different text prompts to build a text-driven contrastive learning strategy. On the other hand, we introduce a noise-driven consistency regularization to align local instances with diffusion denoising representations, constraining the optimization region in the feature space. In addition, FedDifRC can be extended to a self-supervised scheme without relying on any labeled data. We also provide a theoretical analysis for FedDifRC to ensure convergence under non-convex objectives. The experiments on different scenarios validate the effectiveness of FedDifRC and the efficiency of crucial components.
Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting
Authors: Jiaxin Huang; Sheng Miao; Bangbang Yang; Yuewen Ma; Yiyi Liao
Venue: ICCV 2025 (main)

Abstract: Reconstructing 4D dynamic scenes from casually captured monocular videos is valuable but highly challenging, as each timestamp is observed from a single viewpoint. We introduce Vivid4D, a novel approach that enhances 4D monocular video synthesis by augmenting observation views -- synthesizing multi-view videos from a monocular input. Unlike existing methods that either solely leverage geometric priors for supervision or use generative priors while overlooking geometry, we integrate both. This reformulates view augmentation as a video inpainting task, where observed views are warped into new viewpoints based on monocular depth priors. To achieve this, we train a video inpainting model on unposed web videos with synthetically generated masks that mimic warping occlusions, ensuring spatially and temporally consistent completion of missing regions. To further mitigate inaccuracies in monocular depth priors, we introduce an iterative view augmentation strategy and a robust reconstruction loss. Experiments demonstrate that our method effectively improves monocular 4D scene reconstruction and completion.
Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration
Authors: Mark Endo; Xiaohan Wang; Serena Yeung-Levy
Venue: ICCV 2025 (main)

Abstract: Recent works on accelerating Vision-Language Models achieve strong performance across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model. Surprisingly, we find that while strong performance is maintained across many tasks, it exhibits drastically different behavior for a subset of vision-centric tasks such as localization. Upon further investigation, we uncover a core issue with the acceleration approach where most tokens towards the top of the image are pruned away. Yet, on many benchmarks aiming to evaluate vision-centric capabilities, strong performance persists with the flawed pruning strategy, highlighting these benchmarks' limited ability to assess fine-grained visual capabilities. Based on these findings, we propose FEATHER (Fast and Effective Acceleration wiTH Ensemble cRiteria), a straightforward approach that resolves the discovered early-layer pruning issue and further enhances the preservation of relevant tokens via multistage pruning with early uniform sampling to ensure broad image coverage. With comparable computational savings, we find that FEATHER achieves more than 5x performance improvement on the vision-centric localization benchmarks compared to the original acceleration approach.