GenAI4ELab/papercli-papers huggingface dataset:
Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
Authors: Renshuai Tao; Haoyu Wang; Yuzhe Guo; Hairong Chen; Li Zhang; Xianglong Liu; Yunchao Wei; Yao Zhao
Venue: CVPR 2025 (main)

Abstract: To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using "expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to +24.7%for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models.
LUCAS: Layered Universal Codec Avatars
Authors: Di Liu; Teng Deng; Giljoo Nam; Yu Rong; Stanislav Pidhorskyi; Junxuan Li; Jason Saragih; Dimitris N. Metaxas; Chen Cao
Venue: CVPR 2025 (main)

Abstract: Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. LUCAS can be integrated with Gaussian Splatting to enhance visual fidelity, a feature particularly beneficial for rendering complex hairstyles. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction.
MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices
Authors: Jianwen Jiang; Gaojie Lin; Zhengkun Rong; Chao Liang; Yongming Zhu; Jiaqi Yang; Tianyun Zhong
Venue: CVPR 2025 (main)

Abstract: Existing neural head avatars methods have achieved significant progress in the image quality and motion range of portrait animation. However, these methods prioritize effectiveness over computational overhead. This paper presents MobilePortrait, a lightweight one-shot neural head avatars method that reduces learning complexity by integrating external knowledge into both the motion modeling and image synthesis, enabling real-time inference on mobile devices. Specifically, We introduce a mixed keypoint representation of explicit and implicit keypoints for precise motion modeling and equip it with precomputed visual features to enhance both facial and background synthesis. With these designs, our model can achieve state-of-the-art performance using UNet with low FLOPs as the backbone, requiring less than 1/10 the computational demand. It has been validated to reach speeds of over 50 FPS on mobile devices and support both video and audio-driven inputs.
D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
Authors: Yongqi Yang; Zhihao Qian; Ye Zhu; Olga Russakovsky; Yu Wu
Venue: CVPR 2025 (main)

Abstract: The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector's ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generalization and robustness. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs and demonstrate the divergence of detectors' performance. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or sacrifice their fitting ability for seen generators (i.e., In-Domain (ID) performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain (OOD) performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D3) framework, whose core idea is to deconstruct the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image feature as an extra discrepancy signal and supplements its original counterpart. Extensive scaled-up experiments demonstrate the effectiveness of D3, achieving 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance. The source code will be updated in our GitHub repository: https://github.com/BigAandSmallq/D3.
Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising
Authors: Yongli Xiang; Ziming Hong; Lina Yao; Dadong Wang; Tongliang Liu
Venue: CVPR 2025 (main)

Abstract: Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains. Recently, well-designed attack, which restores the unauthorized-domain performance by fine-tuning NTL models on few authorized samples, highlights the security risks of NTL-based applications. However, such attack requires modifying model weights, thus being invalid in the black-box scenario. This raises a critical question: can we trust the security of NTL models deployed as black-box systems? In this work, we reveal the first loophole of black-box NTL models by proposing a novel attack method (dubbed as JailNTL) to jailbreak the non-transferable barrier through test-time data disguising, The main idea of JailNTL is to disguise unauthorized data so it can be identified as authorized by the NTL model, thereby bypassing the non-transferable barrier without modifying the NTL model weights. Specifically, JailNTL encourages unauthorized-domain disguising in two levels, including: (i) data-intrinsic disguising (DID) for eliminating domain discrepancy and preserving class-related content at the input-level, and (ii) model-guided disguising (MGD) for mitigating output-level statistics difference of the NTL model. Empirically, when attacking state-of-the-art (SOTA) NTL models in the black-box scenario, JailNTL achieves an accuracy increase of up to 55.7% in the unauthorized domain by using only 1% authorized samples, largely exceeding existing SOTA white-box attacks. Code is released at https://github.com/tmllab/2025_CVPR_JailNTL.
Light3R-SfM: Towards Feed-forward Structure-from-Motion
Authors: Sven Elflein; Qunjie Zhou; Laura Leal-Taixé
Venue: CVPR 2025 (main)

Abstract: We present Light3R-SfM, a feed-forward, end-to-end learnable framework for efficient large-scale Structure-from-Motion (SfM) from unconstrained image collections. Unlike existing SfM solutions that rely on costly matching and global optimization to achieve accurate 3D reconstructions, Light3R-SfM addresses this limitation through a novel latent global alignment module. This module replaces traditional global optimization with a learnable attention mechanism, effectively capturing multi-view constraints across images for robust and precise camera pose estimation. Light3R-SfM constructs a sparse scene graph via retrieval-score-guided shortest path tree to dramatically reduce memory usage and computational overhead compared to the naive approach. Extensive experiments demonstrate that Light3R-SfM achieves competitive accuracy while significantly reducing runtime, making it ideal for 3D reconstruction tasks in real-world applications with a runtime constraint.
Robotic Visual Instruction
Authors: Yanbang Li; Ziyang Gong; Haoyang Li; Xiaoqi Huang; Haolan Kang; Guangping Bai; Xianzheng Ma
Venue: CVPR 2025 (main)

Abstract: Recently, natural language has been the primary medium for human-robot interaction. However, its inherent lack of spatial precision for robotic control introduces challenges such as ambiguity and verbosity. To address these limitations, we introduce the ***Robotic Visual Instruction (RoVI)***, a novel paradigm to guide robotic tasks through an object-centric, hand-drawn symbolic representation. RoVI effectively encodes spatial-temporal information into human-interpretable visual instructions through 2D sketches, utilizing arrows, circles, colors, and numbers to direct 3D robotic manipulation. To enable robots to understand RoVI better and generate precise actions based on RoVI, we present ***Visual Instruction Embodied Workflow (VIEW)***, a pipeline formulated for RoVI-conditioned policies. This approach leverages Vision-Language Models (VLMs) to interpret RoVI inputs, decode spatial and temporal constraints from 2D pixel space via keypoint extraction, and then transform them into executable 3D action sequences. We additionally curate a specialized dataset of 15K instances to fine-tune small VLMs for edge deployment, enabling them to effectively learn RoVI capabilities. Our approach is rigorously validated across 11 novel tasks in both real and simulated environments, demonstrating significant generalization capability. Notably, VIEW achieves an 87.5% success rate in real-world scenarios involving unseen tasks that feature multi-step actions, with disturbances, and trajectory-following requirements. Code and Datasets in this paper will be released soon.
Solving Instance Detection from an Open-World Perspective
Authors: Qianqian Shen; Yunhan Zhao; Nahyun Kwon; Jeeeun Kim; Yanan Li; Shu Kong
Venue: CVPR 2025 (main)

Abstract: Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to identify all possible object instances, followed by instance-level matching to pinpoint the ones of interest. Its open-world nature supports its broad applications from robotics to AR/VR but also presents significant challenges: methods must generalize to unknown testing data distributions because (1) the testing scene imagery is unseen during training, and (2) there are domain gaps between visual references and detected proposals. Existing methods tackle these challenges by synthesizing diverse training examples or utilizing off-the-shelf foundation models (FMs). However, they only partially capitalize the available open-world information. In contrast, we approach InsDet from an Open-World perspective, introducing our method IDOW. We find that, while pretrained FMs yield high recall in instance detection, they are not specifically optimized for instance-level feature matching. Therefore, we adapt pretrained FMs for improved instance-level matching using open-world data. Our approach incorporates metric learning along with novel data augmentations, which sample distractors as negative examples and synthesize novel-view instances to enrich the visual references. Extensive experiments demonstrate that our method significantly outperforms prior works, achieving >10 AP over previous results on two recently released challenging benchmark datasets in both conventional and novel instance detection settings.
Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection
Authors: Aming Wu; Cheng Deng
Venue: CVPR 2025 (main)

Abstract: To accelerate the safe deployment of object detectors, we focus on reducing the impact of both covariate and semantic shifts. And we consider a realistic yet challenging scenario, namely Open-Domain Unknown Object Detection (ODU-OD), which aims to detect unknown objects in unseen target domains without accessing any auxiliary data. Towards ODU-OD, it is feasible to learn a robust discriminative boundary by synthesizing virtual features. Generally, perception, memory, and imagination are three essential capacities for human beings. Through multi-level perception and rich memory about known objects, the characteristics of unknown objects can be imagined sufficiently, enhancing the ability of discriminating known from unknown objects. Inspired by this idea, an approach of World Feature Simulation (WFS) is proposed, mainly consisting of a multi-level perception, memory recorder, and unknown-feature generator. Specifically, after extracting the features of the input, we separately employ a Mamba and Graph Network to obtain the global-level and connective-level representations. Next, a codebook containing multiple learnable codewords is defined to preserve fragmented memory of known objects. Meanwhile, we perform a modulated operation on the memory to form the imagination bank involving unknown characteristics. Finally, to alleviate the impact of lacking supervision data, based on the multi-level representation and imagination bank, a dedicated unknown-feature generator is designed to recurrently synthesize outlier features deviating from in-distribution (ID) objects. The significant performance gains on four different detection tasks demonstrate the superiorities of our method. The code will be released at https://github.com/AmingWu/WFS.
Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses
Authors: Yongfan Liu; Hyoukjun Kwon
Venue: CVPR 2025 (main)

Abstract: Stereo depth estimation is a fundamental component in augmented reality (AR), which requires low latency for real-time processing. However, preprocessing such as rectification and non-ML computations such as cost volume require significant amount of latency exceeding that of an ML model itself, which hinders the real-time processing required by AR. Therefore, we develop alternative approaches to the rectification and cost volume that consider ML acceleration (GPU and NPUs) in recent hardware. For pre-processing, we eliminate it by introducing homography matrix prediction network with a rectification positional encoding (RPE), which delivers both low latency and robustness to unrectified images. For cost volume, we replace it with a group-pointwise convolution-based operator and approximation of cosine similarity based on layernorm and dot product. Based on our approaches, we develop MultiHeadDepth (replacing cost volume) and HomoDepth (MultiHeadDepth + removing pre-processing) models. MultiHeadDepth provides 11.8-30.3% improvements in accuracy and 22.9-25.2% reduction in latency compared to a state-of-the-art depth estimation model for AR glasses from industry. HomoDepth, which can directly process unrectified images, reduces the end-to-end latency by 44.5%. We also introduce a multi-task learning method to handle misaligned stereo inputs on HomoDepth, which reduces the AbsRel error by 10.0-24.3%. The overall results demonstrate the efficacy of our approaches, which not only reduce the inference latency but also improve the model performance. Our code is available at https://github.com/UCI-ISA-Lab/MultiHeadDepth-HomoDepth
3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination
Authors: Jianing Yang; Xuweiyi Chen; Nikhil Madaan; Madhavan Iyengar; Shengyi Qian; David F. Fouhey; Joyce Chai
Venue: CVPR 2025 (main)

Abstract: The integration of language and 3D perception is crucial for embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is a lack of large-scale datasets with dense grounding between language and 3D scenes. We introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons of models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the importance of large-scale 3D-text datasets for embodied AI research. Our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with resources and insights to lead to more reliable and better-grounded 3D-LLMs.
LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation
Authors: Chenxu Zhou; Lvchang Fu; Sida Peng; Yunzhi Yan; Zhanhua Zhang; Yong Chen; Jiazhi Xia; Xiaowei Zhou
Venue: CVPR 2025 (main)

Abstract: This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot perform real-time LiDAR rendering. To overcome these constraints, we propose LiDAR-RT, a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes. Our primary contribution is the development of an efficient and effective rendering pipeline, which integrates Gaussian primitives and hardware-accelerated ray tracing technology. Specifically, we model the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters and incorporate scene graphs to handle scene dynamics. Building upon this scene representation, our framework first constructs a bounding volume hierarchy (BVH), then casts rays for each pixel and generates novel LiDAR views through a differentiable rendering algorithm. Importantly, our framework supports realistic rendering with flexible scene editing operations and various sensor configurations. Extensive experiments across multiple public benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of rendering quality and efficiency. Codes will be released to ensure reproducibility.
Generative Zero-Shot Composed Image Retrieval
Authors: Lan Wang; Wei Ao; Vishnu Naresh Boddeti; Ser-Nam Lim
Venue: CVPR 2025 (main)

Abstract: Composed Image Retrieval (CIR) is a vision-language task utilizing queries comprising images and textual descriptions to achieve precise image retrieval. This task seeks to find images that are visually similar to a reference image while incorporating specific changes or features described textually (visual delta). CIR enables a more flexible and user-specific retrieval by bridging visual data with verbal instructions. This paper introduces a novel generative method that augments Composed Image Retrieval by Composed Image Generation (CIG) to provide pseudo-target images. CIG utilizes a textual inversion network to map reference images into semantic word space, which generates pseudo-target images in combination with textual descriptions. These images serve as additional visual information, significantly improving the accuracy and relevance of retrieved images when integrated into existing retrieval frameworks. Experiments conducted across multiple CIR datasets and several baseline methods demonstrate improvements in retrieval performance, which shows the potential of our approach as an effective add-on for existing composed image retrieval.
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
Authors: Chaehun Shin; Jooyoung Choi; Heeseung Kim; Sungroh Yoon
Venue: CVPR 2025 (main)

Abstract: Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while recent zero-shot approaches leverage on-the-fly image prompting, often sacrificing subject alignment. In this paper, we introduce Diptych Prompting, a novel zero-shot approach that reinterprets as an inpainting task with precise subject alignment by leveraging the emergent property of diptych generation in large-scale text-to-image models. Diptych Prompting arranges an incomplete diptych with the reference image in the left panel, and performs text-conditioned inpainting on the right panel. We further prevent unwanted content leakage by removing the background in the reference image and improve fine-grained details in the generated subject by enhancing attention weights between the panels during inpainting. Experimental results confirm that our approach significantly outperforms zero-shot image prompting methods, resulting in images that are visually preferred by users. Additionally, our method supports not only subject-driven generation but also stylized image generation and subject-driven image editing, demonstrating versatility across diverse image generation applications.
MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
Authors: Riku Murai; Eric Dexheimer; Andrew J. Davison
Venue: CVPR 2025 (main)

Abstract: We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior.  Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre.  We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks.  Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally consistent poses and dense geometry while operating at 15 FPS.