GenAI4ELab/papercli-papers huggingface dataset:
AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering
Authors: Jing Wang; Songhe Feng; Kristoffer Knutsen Wickstrøm; Michael C. Kampffmeyer
Venue: CVPR 2025 (main)

Abstract: Most Multi-view Clustering approaches assume that all views are available for clustering. However, this assumption is often unrealistic as views are incrementally accumulated over time, leading to a need for continual multi-view clustering (CMVC) methods. Current approaches to CMVC leverage late fusion-based approaches, where a new model is typically learned individually for each view to obtain the corresponding partition matrix, and then used to update a consensus matrix via a moving average. These approaches are prone to view-specific noise and struggle to adapt to large gaps between different views. To address these shortcomings, we reconsider CMVC from the perspective of domain adaptation and propose AdaptCMVC, which learns how to incrementally accumulate knowledge of new views as they become available and prevents catastrophic forgetting. Specifically, a self-training framework is introduced to extend the model to new views, particularly designed to be robust to view-specific noise. Further, to combat catastrophic forgetting, a structure alignment mechanism is proposed to enable the model to explore the global group structure across multiple views. Experiments on several multi-view benchmarks demonstrate the effectiveness of our proposed method on the CMVC task. The code is available at: AdaptCMVC.
Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
Authors: Dongxu Wei; Zhiqi Li; Peidong Liu
Venue: CVPR 2025 (main)

Abstract: Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction. Our code is available at https://github.com/WU-CVGL/Omni-Scene.
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
Authors: Zhaoxi Chen; Jiaxiang Tang; Yuhao Dong; Ziang Cao; Fangzhou Hong; Yushi Lan; Tengfei Wang; Haozhe Xie; Tong Wu; Shunsuke Saito; Liang Pan; Dahua Lin; Ziwei Liu
Venue: CVPR 2025 (main)

Abstract: The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. Extensive qualitative and quantitative experiments are conducted to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications.
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
Authors: Ming-Feng Li; Xin Yang; Fu-En Wang; Hritam Basak; Yuyin Sun; Shreekant Gayaka; Min Sun; Cheng-Hao Kuo
Venue: CVPR 2025 (main)

Abstract: 6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous reference images that fully cover the object. Estimating 6D poses from partial references, which capture only fragments of an object's appearance and geometry, remains challenging. To address this, we propose UA-Pose, an uncertainty-aware approach for 6D object pose estimation and online object completion specifically designed for partial references. We assume access to either (1) a limited set of RGBD images with known poses or (2) a single 2D image. For the first case, we initialize a partial object 3D model based on the provided images and poses, while for the second, we use image-to-3D techniques to generate an initial object 3D model. Our method integrates uncertainty into the incomplete 3D model, distinguishing between seen and unseen regions. This uncertainty enables confidence assessment in pose estimation and guides an uncertainty-aware sampling strategy for online object completion, enhancing robustness in pose estimation accuracy and improving object completeness. We evaluate our method on the YCB-Video, YCBInEOAT, and HO3D datasets, including RGBD sequences of YCB objects manipulated by robots and human hands. Experimental results demonstrate significant performance improvements over existing methods, particularly when object observations are incomplete or partially captured.
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval
Authors: Yuanmin Tang; Jing Yu; Keke Gai; Jiamin Zhuang; Gang Xiong; Gaopeng Gou; Qi Wu
Venue: CVPR 2025 (main)

Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essential target content. In this paper, we propose a novel prediction-based mapping network, named PrediCIR, to adaptively predict the missing target visual content in reference images in the latent space before mapping for accurate ZS-CIR. Specifically, a world view generation module first constructs a source view by omitting certain visual content of a target view, coupled with an action that includes the manipulation intent derived from existing image-caption pairs. Then, a target content prediction module trains a world model as a predictor to adaptively predict the missing visual information guided by user intention in manipulating text at the latent space. The two modules map an image with the predicted relevant information to a pseudo-word token without extra supervision. Our model shows strong generalization ability on six ZS-CIR tasks. It obtains consistent and significant performance boosts ranging from 1.73% to 4.45% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/predicir.
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
Authors: Shiyang Zhou; Haijin Zeng; Yunfan Lu; Tong Shao; Ke Tang; Yongyong Chen; Jie Liu; Jingyong Su
Venue: CVPR 2025 (main)

Abstract: Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods that leverage long-range dependency modeling have achieved promising results, their complexity severely limits deployment on mobile devices for real-world applications. To address these limitations, we propose a lightweight Mamba-based binary neural network designed for efficient and high-performing demosaicing of HybridEVS RAW images. First, to effectively capture both global and local dependencies, we introduce a hybrid Binarized Mamba-Transformer architecture that combines the strengths of the Mamba and Swin Transformer architectures. Next, to significantly reduce computational complexity, we propose a binarized Mamba (Bi-Mamba), which binarizes all projections while retaining the core Selective Scan in full precision. Bi-Mamba also incorporates additional global visual information to enhance global context and mitigate precision loss. We conduct quantitative and qualitative experiments to demonstrate the effectiveness of BMTNet in both performance and computational efficiency, providing a lightweight demosaicing solution suited for real-world edge devices. Our codes and models are available at https://github.com/Clausy9/BMTNet.
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation
Authors: Jianzong Wu; Chao Tang; Jingbo Wang; Yanhong Zeng; Xiangtai Li; Yunhai Tong
Venue: CVPR 2025 (main)

Abstract: Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose a new task: customized manga generation and introduce DiffSensei, an innovative framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer. Additionally, the MLLM-based adapter adjusts character features to align with panel-specific text cues, allowing flexible adjustments in character expressions, poses, and actions. We also introduce MangaZero, a large-scale dataset tailored to this task, containing 43,264 manga pages and 427,147 annotated panels, supporting the visualization of varied character interactions and movements across sequential frames. Extensive experiments demonstrate that DiffSensei outperforms existing models, marking a significant advancement in manga generation by enabling text-adaptable character customization. The code, model, and dataset will be open-sourced to the community.
Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions
Authors: Chan Hur; Jeong-hun Hong; Dong-hun Lee; Dabin Kang; Semin Myeong; Sang-hyo Park; Hyeyoung Park
Venue: CVPR 2025 (main)

Abstract: In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.
IDEA-Bench: How Far are Generative Models from Professional Designing?
Authors: Chen Liang; Lianghua Huang; Jingwu Fang; Huanzhang Dou; Wei Wang; Zhi-Fan Wu; Yupeng Shi; Junge Zhang; Xin Zhao; Yu Liu
Venue: CVPR 2025 (main)

Abstract: Recent advancements in image generation models enable the creation of high-quality images and targeted modifications based on textual instructions. Some models even support multimodal complex guidance and demonstrate robust task generalization capabilities. However, they still fall short of meeting the nuanced, professional demands of designers. To bridge this gap, we introduce IDEA-Bench, a comprehensive benchmark designed to advance image generation models toward applications with robust task generalization. IDEA-Bench comprises 100 professional image generation tasks and 275 specific cases, categorized into five major types based on the current capabilities of existing models. Furthermore, we provide a representative subset of 18 tasks with enhanced evaluation criteria to facilitate more nuanced and reliable evaluations using Multimodal Large Language Models (MLLMs). By assessing models' ability to comprehend and execute novel, complex tasks, IDEA-Bench paves the way toward the development of generative models with autonomous and versatile visual generation capabilities.
Interpretable Image Classification via Non-parametric Part Prototype Learning
Authors: Zhijie Zhu; Lei Fan; Maurice Pagnucco; Yang Song
Venue: CVPR 2025 (main)

Abstract: Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability.
PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset
Authors: Jiazhen Liu; Yuhan Fu; Ruobing Xie; Runquan Xie; Xingwu Sun; Fengzong Lian; Zhanhui Kang; Xirong Li
Venue: CVPR 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e. task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with specious context (PhD-sec) or with incorrect context (PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal modules: task-specific hallucinatory item (hitem) selection, hitem-embedded question generation, specious / incorrect context generation, and counter-common-sense (CCS) image generation. With over 14k daily images, 750 CCS images and 102k VQA triplets in total, PhD reveals considerable variability in MLLMs' performance across various modes and tasks, offering valuable insights into the nature of hallucination. As such, PhD stands as a potent tool not only for VHE but may also play a significant role in the refinement of MLLMs.
CARL: A Framework for Equivariant Image Registration
Authors: Hastings Greer; Lin Tian; François-Xavier Vialard; Roland Kwitt; Raul San Jose Estepar; Marc Niethammer
Venue: CVPR 2025 (main)

Abstract: Image registration estimates spatial correspondences between image pairs. These estimates are typically obtained via numerical optimization or regression by a deep network. A desirable property is that a correspondence estimate (e.g., the true oracle correspondence) for an image pair is maintained under deformations of the input images. Formally, the estimator should be equivariant to a desired class of image transformations. In this work, we present careful analyses of equivariance properties in the context of multi-step deep registration networks. Based on these analyses we 1) introduce the notions of [U,U] equivariance (network equivariance to the same deformations of the input images) and [W,U] equivariance (where input images can undergo different deformations); we 2) show that in a suitable multi-step registration setup it is sufficient for overall [W,U] equivariance if the first step has [W,U] equivariance and all others have [U,U] equivariance; we 3) show that common displacement-predicting networks only exhibit [U,U] equivariance to translations instead of the more powerful [W,U] equivariance; and we 4) show how to achieve multi-step [W,U] equivariance via a coordinate-attention mechanism combined with displacement-predicting networks. Our approach obtains excellent practical performance for 3D abdomen, lung, and brain medical image registration. We match or outperform state-of-the-art (SOTA) registration approaches on all the datasets with a particularly strong performance for the challening abdomen registration.
ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate
Authors: Ming Yan; Xincheng Lin; Yuhua Luo; Shuqi Fan; Yudi Dai; Qixin Zhong; Lincai Zhong; Yuexin Ma; Lan Xu; Chenglu Wen; Siqi Shen; Cheng Wang
Venue: CVPR 2025 (main)

Abstract: Human Motion Recovery (HMR) research mainly focuses on ground-based motions such as running. The study on capturing climbing motion, an off-ground motion, is sparse. This is partly due to the limited availability of climbing motion datasets, especially large-scale and challenging 3D labeled datasets. To address the insufficiency of climbing motion datasets, we collect AscendMotion, a large-scale well-annotated, and challenging climbing motion dataset. It consists of 412k RGB, LiDAR frames, and IMU measurements, which includes the challenging climbing motions of 22 professional climbing coaches across 12 different rocks. Capturing the climbing motions is challenging as it requires precise recovery of not only the complex pose but also the global position of climbers. Although multiple global HMR methods have been proposed, they cannot faithfully capture climbing motions. To address the limitations of HMR methods for climbing, we propose ClimbingCap, a motion recovery method that reconstructs continuous 3D human climbing motion in a global coordinate system. One key insight is to use the RGB and the LiDAR modalities to separately reconstruct motions in camera coordinates and global coordinates and optimize them jointly. We demonstrate the quality of the AscendMotion dataset and present promising results from ClimbingCap. The AscendMotion dataset and the source code of ClimbingCap will be released publicly to the research community.
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh
Authors: Jingyu Zhuang; Di Kang; Linchao Bao; Liang Lin; Guanbin Li
Venue: CVPR 2025 (main)

Abstract: Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitations above, we propose DAGSM, a novel pipeline that generates disentangled human bodies and garments from the given text prompts. Specifically, we model each part (e.g., body, upper/lower clothes) of the clothed human as one GS-enhanced mesh (GSM), which is a traditional mesh attached with 2D Gaussians to better handle complicated textures (e.g., woolen, translucent clothes) and produce realistic cloth animations. During the generation, we first create the unclothed body, followed by a sequence of individual cloth generation based on the body, where we introduce a semantic-based algorithm to achieve better human-cloth and garment-garment separation. To improve texture quality, we propose a view-consistent texture refinement module, including a cross-view attention mechanism for texture style consistency and an incident-angle-weighted denoising (IAW-DE) strategy to update the appearance. Extensive experiments have demonstrated that DAGSM generates high-quality disentangled avatars, supports clothing replacement and realistic animation, and outperforms the baselines in visual quality.
Estimating Body and Hand Motion in an Ego-sensed World
Authors: Brent Yi; Vickie Ye; Maya Zheng; Yunqi Li; Lea Müller; Georgios Pavlakos; Yi Ma; Jitendra Malik; Angjoo Kanazawa
Venue: CVPR 2025 (main)

Abstract: We present EgoAllo, a system for human motion estimation from a head-mounted device. Using only egocentric SLAM poses and images, EgoAllo guides sampling from a conditional diffusion model to estimate 3D body pose, height, and hand parameters that capture a device wearer's actions in the allocentric coordinate frame of the scene. To achieve this, our key insight is in representation: we propose spatial and temporal invariance criteria for improving model performance, from which we derive a head motion conditioning parameterization that improves estimation by up to 18%. We also show how the bodies estimated by our system can improve hand estimation: the resulting kinematic and temporal constraints can reduce world-frame errors in single-frame estimates by 40%.