GenAI4ELab/papercli-papers huggingface dataset:
C4D: 4D Made from 3D through Dual Correspondences
Authors: Shizun Wang; Zhenxiang Jiang; Xingyi Yang; Xinchao Wang
Venue: ICCV 2025 (main)

Abstract: Recovering 4D from monocular video, which jointly estimates dynamic geometry and camera poses, is an inevitably challenging problem. While recent pointmap-based 3D reconstruction methods (e.g., DUSt3R) have made great progress in reconstructing static scenes, directly applying them to dynamic scenes leads to inaccurate results. This discrepancy arises because moving objects violate multi-view geometric constraints, disrupting the reconstruction. To address this, we introduce C4D, a framework that leverages temporal Correspondences to extend existing 3D reconstruction formulation to 4D. Specifically, apart from predicting pointmaps, C4D captures two types of correspondences: short-term optical flow and long-term point tracking. We train a dynamic-aware point tracker that provides additional mobility information, facilitating the estimation of motion masks to separate moving elements from the static background, thus offering more reliable guidance for dynamic scenes. Furthermore, we introduce a set of dynamic scene optimization objectives to recover per-frame 3D geometry and camera parameters. Simultaneously, the correspondences lift 2D trajectories into smooth 3D trajectories, enabling fully integrated 4D reconstruction. Experiments show that our framework achieves complete 4D recovery and demonstrates strong performance across multiple downstream tasks, including depth estimation, camera pose estimation, and point tracking.
An Efficient Hybrid Vision Transformer for TinyML Applications
Authors: Fanhong Zeng; Huanan Li; Juntao Guan; Rui Fan; Tong Wu; Xilong Wang; Rui Lai
Venue: ICCV 2025 (main)

Abstract: To enable the deployment of Vision Transformers on resource-constrained mobile and edge devices, the development of efficient ViT models has attracted significant attention. Researchers achieved remarkable improvements in accuracy and speed by optimizing attention mechanisms and integrating lightweight CNN modules. However, existing designs often overlook runtime overhead from memory-bound operations and the shift in feature characteristics from spatial-dominant to semantic-dominant as networks deepen. This work introduces TinyNeXt, a family of efficient hybrid ViTs for TinyML, featuring Lean Single-Head Self-Attention to minimize memory-bound operations, and a macro design tailored to feature characteristics at different stages. TinyNeXt strikes a better accuracy-speed trade-off across diverse tasks and hardware platforms, outperforming state-of-the-art models of comparable scale. For instance, our TinyNeXt-T achieves a remarkable 71.5% top-1 accuracy with only 1.0M parameters on ImageNet-1K. Furthermore, compared to recent efficient models like MobileViT-XXS and MobileViT-XS, TinyNeXt-S and TinyNeXt-M achieve 3.7/0.5 higher accuracy, respectively, while running 2.1x/2.6x faster on Nvidia Jetson Nano. The code and trained models are available at https://github.com/yuffeenn/TinyNeXt.
Object-centric Video Question Answering with Visual Grounding and Referring
Authors: Haochen Wang; Qirui Chen; Cilin Yan; Jiayin Cai; Xiaolong Jiang; Yao Hu; Weidi Xie; Stratis Gavves
Venue: ICCV 2025 (main)

Abstract: Video Large Language Models (VideoLLMs) have recently demonstrated remarkable progress in general video understanding. However, existing models primarily focus on high-level comprehension and are limited to text-only responses, restricting the flexibility for object-centric, multi-round interactions. In this paper, we make three contributions:(i) we address these limitations by introducing a VideoLLM, termed as **RGA3**, capable of performing both object referring and grounding for video reasoning tasks in a multi-round conversational manner, i.e., allowing users to iteratively interact with videos using both textual and visual queries; (ii) we propose **STOM** (Spatial-Temporal Overlay Module), a novel approach that allows arbitrary visual prompts to be processed at any timestamp within a video;(iii) we present **VideoInfer**, a manually curated object-centric video instruction dataset featuring question-answering pairs that require reasoning. We conduct comprehensive experiments on VideoInfer and other existing benchmarks across video question answering and referring video object segmentation. The results on 12 benchmarks spanning 6 tasks show that RGA3 consistently outperforms baseline models in both video question answering and segmentation, underscoring its robustness in multimodal, object-centric video and image understanding. The code, dataset, and web demo will be publicly released.
Face Retouching with Diffusion Data Generation and Spectral Restorement
Authors: Zhidan Xu; Xiaoqin Zhang; Shijian Lu
Venue: ICCV 2025 (main)

Abstract: Face retouching has achieved impressive performance largely driven by its wide range of applications in various real-world tasks. However, most existing works often encounters a dilemma between global consistency and local detail preservation, partially due to the lack of large-scale and high-quality training data. We address the face retouching challenge from two perspectives. First, we create a large-scale face retouching benchmark to mitigate the data scarcity issue. The benchmark comprises 25,000 pairs of high-quality facial images (before and after face retouching) that contain a variety of facial attributes and blemish types such as acne and moles. Second, we design a novel framework that introduces frequency selection and restoration (FSR) and multi-resolution fusion (MRF) that leverages frequency-aware dynamic aggregation and spatial-frequency filtering to achieve global consistency and local detail preservation concurrently. Inspired by the principle of JPEG compression, FSR introduces frequency-domain quantization with spatial projections to learn enhanced feature representations. MRF fuses multi-resolution features via laplacian pyramid fusion, removing large-area blemishes and preserving local fine details effectively. Extensive experiments over multiple benchmarks show that the proposed framework outperforms the state-of-the-art quantitatively and qualitatively. The created benchmark also provides valuable data for training and evaluating both existing and future face retouching networks.
AG2aussian: Anchor-Graph Structured Gaussian Splatting for Instance-Level 3D Scene Understanding and Editing
Authors: Zhaonan Wang; Manyi Li; Changhe Tu
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) has witnessed exponential adoption across diverse applications, driving a critical need for semantic-aware 3D Gaussian representations to enable scene understanding and editing tasks. Existing approaches typically attach semantic features to a collection of free Gaussians and distill the features via differentiable rendering, leading to noisy segmentation and a messy selection of Gaussians. In this paper, we introduce AG^2aussian, a novel framework that leverages an anchor-graph structure to organize semantic features and regulate Gaussian primitives. Our anchor-graph structure not only promotes compact and instance-aware Gaussian distributions, but also facilitates graph-based propagation, achieving a clean and accurate instance-level Gaussian selection. Extensive validation across four applications, i.e. interactive click-based query, open-vocabulary text-driven query, object removal editing, and physics simulation, demonstrates the advantages of our approach and its benefits to various applications. The experiments and ablation studies further evaluate the effectiveness of the key designs of our approach.
Beyond Blur: A Fluid Perspective on Generative Diffusion Models
Authors: Grzegorz Gruszczynski; Jakub Meixner; Michal Wlodarczyk; Przemyslaw Musialski
Venue: ICCV 2025 (main)

Abstract: We propose a novel, Partial Differential Equation (PDE) driven, corruption process for generative image synthesis which generalizes existing PDE-based approaches. Our forward pass formulates image corruption via a physically motivated PDE that couples directional advection with isotropic diffusion and Gaussian noise, controlled by dimensionless numbers. We solve this PDE numerically through a GPU-accelerated Lattice Boltzmann solver for fast evaluation. To induce realistic "turbulence," we generate stochastic velocity fields that introduce coherent motion and capture multi-scale mixing. In the generative process, a neural network learns to reverse the advection-diffusion operator thus constituting a novel generative model.We discuss how previous methods emerge as specific cases of our operator, demonstrating that our framework generalizes prior PDE-based corruption techniques. We illustrate how advection improves the diversity and quality of the generated images while keeping the overall color palette unaffected. This work bridges fluid dynamics, dimensionless PDE theory, and deep generative modeling, offering a fresh perspective on physically based inverse problems.
CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation
Authors: Xiangyang Luo; Ye Zhu; Yunfei Liu; Lijian Lin; Cong Wan; Zijian Cai; Yu Li; Shao-Lun Huang
Venue: ICCV 2025 (main)

Abstract: Video face swapping aims to address two primary challenges: effectively transferring the source identity to the target video and accurately preserving the dynamic attributes of the target face, such as head poses, facial expressions, lip-sync, etc..Existing methods mainly focus on achieving high-quality identity transfer but often fall short in maintaining the dynamic attributes of the target face, leading to inconsistent results. We attribute this issue to the inherent coupling of facial appearance and motion in videos. To address this, we propose CanonSwap, a novel video face-swapping framework that decouples motion information from appearance information. Specifically, CanonSwap first eliminates motion-related information, enabling identity modification within a unified canonical space. Subsequently, the swapped identity is reintegrated into the original video space, ensuring the preservation of the target face's dynamic attributes. To further achieve precise identity transfer with minimal artifacts and enhanced realism, we design a Partial Identity Modulation module that adaptively integrates source identity features using a spatial mask to restrict modifications to facial regions.Additionally, we introduce several fine-grained synchronization metrics to comprehensively evaluate the performance of video face swapping methods.Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of visual quality, temporal consistency, and identity preservation.
Soft Local Completeness: Rethinking Completeness in XAI
Authors: Ziv Weiss Haddad; Oren Barkan; Yehonatan  Elisha; Noam Koenigstein
Venue: ICCV 2025 (main)

Abstract: Completeness is a widely discussed property in explainability research, requiring that the attributions sum to the model's response to the input. While completeness intuitively suggests that the model's prediction is "completely explained" by the attributions, its global formulation alone is insufficient to ensure faithful explanations. We contend that promoting completeness locally within attribution subregions, in a soft manner, can serve as a standalone guiding principle for producing faithful attributions. To this end, we introduce the concept of the completeness gap as a flexible measure of completeness and propose an optimization procedure that minimizes this gap across subregions within the attribution map. Extensive evaluations across various model architectures demonstrate that our method produces state-of-the-art results.
Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions
Authors: Liang Xu; Chengqun Yang; Zili Lin; Fei Xu; Yifan Liu; Congsheng Xu; Yiyi Zhang; Jie Qin; Xingdong Sheng; Yunhui Liu; Xin Jin; Yichao Yan; Wenjun Zeng; Xiaokang Yang
Venue: ICCV 2025 (main)

Abstract: Learning action models from real-world human-centric interaction datasets is important towards building general-purpose intelligent assistants with efficiency. However, most existing datasets only offer specialist interaction category and ignore that AI assistants perceive and act based on first-person acquisition. We urge that both the generalist interaction knowledge and egocentric modality are indispensable. In this paper, we embed the manual-assisted task into a vision-language-action framework, where the assistant provides services to the instructor following egocentric vision and commands. With our hybrid RGB-MoCap system, pairs of assistants and instructors engage with multiple objects and the scene following GPT-generated scripts. Under this setting, we accomplish InterVLA, the first large-scale human-object-human interaction dataset with 11.4 hours and 1.2M frames of multimodal data, spanning 2 egocentric and 5 exocentric videos, accurate human/object motions and verbal commands. Furthermore, we establish novel benchmarks on egocentric human motion estimation, interaction synthesis, and interaction prediction with comprehensive analysis. We believe that our InterVLA testbed and the benchmarks will foster future works on building AI agents in the physical world.
Open-ended Hierarchical Streaming Video Understanding with Vision Language Models
Authors: Hyolim Kang; Yunsu Park; Youngbeom Yoo; Yeeun Choi; Seon Joo Kim
Venue: ICCV 2025 (main)

Abstract: We introduce Hierarchical Streaming Video Understanding, a task that combines online temporal action localization with free-form description generation. Given the scarcity of datasets with hierarchical and fine-grained temporal annotations, we demonstrate that LLMs can effectively group atomic actions into higher-level events, enriching existing datasets.We then propose OpenHOUSE (Open-ended Hierarchical Online Understanding System for Events), which extends streaming action perception beyond action classification. OpenHOUSE features a specialized streaming module that accurately detects boundaries between closely adjacent actions, nearly doubling the performance of direct extensions of existing methods.We envision the future of streaming action perception in the integration of powerful generative models, with OpenHOUSE representing a key step in that direction.
SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Authors: Jessica Bader; Leander Girrbach; Stephan Alaniz; Zeynep Akata
Venue: ICCV 2025 (main)

Abstract: Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medicine. Despite their success, we demonstrate that CBMs struggle to reliably identify the correct concepts under distribution shifts. To assess the robustness of CBMs to concept variations, we introduce SUB: a fine-grained image and concept benchmark containing 38,400 synthetic images based on the CUB dataset. To create SUB, we select a CUB subset of 33 bird classes and 45 concepts to generate images which substitute a specific concept, such as wing color or belly pattern. We introduce a novel Tied Diffusion Guidance (TDG) method to precisely control generated images, where noise sharing for two parallel denoising processes ensures that both the correct bird class and the correct attribute are generated. This novel benchmark enables rigorous evaluation of CBMs and similar interpretable models, contributing to the development of more robust methods. Our code is available at https://github.com/ExplainableML/sub and the dataset at http://huggingface.co/datasets/Jessica-bader/SUB.
G-DexGrasp: Generalizable Dexterous Grasping Synthesis Via Part-Aware Prior Retrieval and Prior-Assisted Generation
Authors: Juntao Jian; Xiuping Liu; Zixuan Chen; Manyi Li; Jian Liu; Ruizhen Hu
Venue: ICCV 2025 (main)

Abstract: Recent advances in dexterous grasping synthesis have demonstrated significant progress in producing reasonable and plausible grasps for many task purposes. But it remains challenging to generalize to unseen object categories and diverse task instructions. In this paper, we propose G-DexGrasp, a retrieval-augmented generation approach that can produce high-quality dexterous hand configurations for unseen object categories and language-based task instructions. The key is to retrieve generalizable grasping priors, including the fine-grained contact part and the affordance-related distribution of relevant grasping instances, for the following synthesis pipeline. Specifically, the fine-grained contact part and affordance act as generalizable guidance to infer reasonable grasping configurations for unseen objects with a generative model, while the relevant grasping distribution plays as regularization to guarantee the plausibility of synthesized grasps during the subsequent refinement optimization. Our comparison experiments validate the effectiveness of our key designs for generalization and demonstrate the remarkable performance against the existing approaches.
Autoregressive Denoising Score Matching is a Good Video Anomaly Detector
Authors: Hanwen Zhang; Congqi Cao; Qinyi Lv; Lingtong Min; Yanning Zhang
Venue: ICCV 2025 (main)

Abstract: Video anomaly detection (VAD) is an important computer vision problem. Thanks to the mode coverage capabilities of generative models, the likelihood-based paradigm is catching growing interest, as it can model normal distribution and detect out-of-distribution anomalies. However, these likelihood-based methods are blind to the anomalies located in local modes near the learned distribution. To handle these "unseen" anomalies, we dive into three gaps uniquely existing in VAD regarding scene, motion and appearance. Specifically, we first build a noise-conditioned score transformer for denoising score matching. Then, we introduce a scene-dependent and motion-aware score function by embedding the scene condition of input sequences into our model and assigning motion weights based on the difference between key frames of input sequences. Next, to solve the problem of blindness in principle, we integrate unaffected visual information via a novel autoregressive denoising score matching mechanism for inference. Through autoregressively injecting intensifying Gaussian noise into the denoised data and estimating the corresponding score function, we compare the denoised data with the original data to get a difference and aggregate it with the score function for an enhanced appearance perception and accumulate the abnormal context. With all three gaps considered, we can compute a more comprehensive anomaly indicator. Experiments on three popular VAD benchmarks demonstrate the state-of-the-art performance of our method.
LUDVIG: Learning-Free Uplifting of 2D Visual Features to Gaussian Splatting Scenes
Authors: Juliette Marrie; Romain Menegaux; Michael Arbel; Diane Larlus; Julien Mairal
Venue: ICCV 2025 (main)

Abstract: We address the problem of extending the capabilities of vision foundation models such as DINO, SAM, and CLIP, to 3D tasks. Specifically, we introduce a novel method to uplift 2D image features into Gaussian Splatting representations of 3D scenes. Unlike traditional approaches that rely on minimizing a reconstruction loss, our method employs a simpler and more efficient feature aggregation technique, augmented by a graph diffusion mechanism. Graph diffusion refines 3D features, such as coarse segmentation masks, by leveraging 3D geometry and pairwise similarities induced by DINOv2. Our approach achieves performance comparable to the state of the art on multiple downstream tasks while delivering significant speed-ups. Notably, we obtain competitive segmentation results using only generic DINOv2 features, despite DINOv2 not being trained on millions of annotated segmentation masks like SAM. When applied to CLIP features, our method demonstrates strong performance in open-vocabulary object segmentation tasks, highlighting the versatility of our approach.
PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image
Authors: Hyeongjin Nam; Donghwan Kim; Gyeongsik Moon; Kyoung Mu Lee
Venue: ICCV 2025 (main)

Abstract: The misaligned human texture across different human parts is one of the main limitations of existing 3D human reconstruction methods. Each human part, such as a jacket or pants, should maintain a distinct texture without blending into others. The structural coherence of human parts serves as a crucial cue to infer human textures in the invisible regions of a single image. However, most existing 3D human reconstruction methods do not explicitly exploit such part segmentation priors, leading to misaligned textures in their reconstructions. In this regard, we present PARTE, which utilizes 3D human part information as a key guide to reconstruct 3D human textures. Our framework comprises two core components. First, to infer 3D human part information from a single image, we propose a 3D part segmentation module (PartSegmenter) that initially reconstructs a textureless human surface and predicts human part labels based on the textureless surface. Second, to incorporate part information into texture reconstruction, we introduce a part-guided texturing module (PartTexturer), which acquires prior knowledge from a pre-trained image generation network on texture alignment of human parts. Extensive experiments demonstrate that our framework achieves state-of-the-art quality in 3D human reconstruction.
LLaVA-SP: Enhancing Visual Representation with Visual Spatial Tokens for MLLMs
Authors: Haoran Lou; Chunxiao Fan; Ziyan Liu; Yuexin Wu; Xinliang Wang
Venue: ICCV 2025 (main)

Abstract: The architecture of multimodal large language models (MLLMs) commonly connects a vision encoder, often based on CLIP-ViT, to a large language model. While CLIP-ViT works well for capturing global image features, it struggles to model local relationships between adjacent patches, leading to weaker visual representation, which in turn affects the detailed understanding ability of MLLMs. To solve this, we propose LLaVA-SP, which only adds six spatial visual tokens to the original visual tokens to enhance the visual representation. Our approach offers three key advantages: 1) We propose a novel Projector, which uses convolutional kernels to derive visual spatial tokens from ViT patch features, simulating two visual spatial ordering approaches: "from central region to global" and "from abstract to specific". Then, a cross-attention mechanism is applied to fuse fine-grained visual information, enriching the overall visual representation. 2) We present two model variants: LLaVA-SP-Cropping, which focuses on detail features through progressive cropping, and LLaVA-SP-Pooling, which captures global semantics through adaptive pooling, enabling the model to handle diverse visual understanding tasks. 3) Extensive experiments show that LLaVA-SP, fine-tuned with LoRA, achieves significant performance improvements across various multimodal benchmarks, outperforming the state-of-the-art LLaVA-1.5 model in multiple tasks with nearly identical inference latency. The code and models are available at https://github.com/CnFaker/LLaVA-SP.