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Infrared Adversarial Car Stickers

Infrared Adversarial Car Stickers

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaopei Zhu Yuqiu Liu Zhanhao Hu Jianmin Li Xiaolin Hu School of Integrated Circuits Tsinghua University Beijing China Department of Technology Beijing Forestry University Beijing China Department of Electrical Engineering and Computer Sciences UC Berkeley California USA Department of Computer Science and Technology Institute for Artificial Intelligence BNRist Tsinghua University Beijing China THBI IDG/McGovern Institute for Brain Research Tsinghua University Beijing China Chinese Institute for Brain Research (CIBR) Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work, we propose a physical attack method against infrared detectors based on 3D modeling, which is applied to a real car. The goal is to design a set of infrared adversarial stickers to make cars invisible to infrared detectors at various viewing angles, distances, and scenes. We build a 3D infrared car model with real infrared characteristics and propose an infrared adversarial pattern generation method based on 3D mesh shadow. We propose a 3D control points-based mesh smoothing algorithm and use a set of smoothness loss functions to enhance the smoothness of adversarial meshes and facilitate the sticker implementation. Besides, We designed the aluminum stickers and conducted physical experiments on two real Mercedes-Benz A200L cars. Our adversarial stickers hid the cars from Faster RCNN, an object detector, at various viewing angles, distances, and scenes. The attack success rate (ASR) was 91.49% for real cars. In comparison, the ASRs of random stickers and no sticker were only 6.21% and 0.66%, respectively. In addition, the ASRs of the designed stickers against six unseen object detectors such as YOLOv3 and Deformable DETR were between 73.35%-95.80%, showing good transferability of the attack performance across detectors.

关键词： Infrared detectors YOLO Solid modeling Three-dimensional displays Smoothing methods Pedestrians Detectors

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FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models

FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimatio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jinglin Xu Yijie Guo Yuxin Peng School of Intelligence Science and Technology University of Science and Technology Beijing Wangxuan Institute of Computer Technology Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to predict human joint coordinates in 3D space. Despite recent advancements in deep learning-based methods, they mostly ignore the capability of coupling accessible texts and naturally feasible knowledge of humans, missing out on valuable implicit supervision to guide the 3D HPE task. Moreover, previous efforts often study this task from the perspective of the whole human body, neglecting fine-grained guidance hidden in different body parts. To this end, we present a new Fine-Grained Prompt-Driven Denoiser based on a diffusion model for 3D HPE, named FinePOSE. It consists of three core blocks enhancing the reverse process of the diffusion model: (1) Fine-grained Part-aware Prompt learning (FPP) block constructs fine-grained part-aware prompts via coupling accessible texts and naturally feasible knowledge of body parts with learnable prompts to model implicit guidance. (2) Fine-grained Prompt-pose Communication (FPC) block establishes fine-grained communications between learned part-aware prompts and poses to improve the denoising quality. (3) Prompt-driven Timestamp Stylization (PTS) block integrates learned prompt embedding and temporal information related to the noise level to enable adaptive adjustment at each denoising step. Extensive experiments on public single-human pose estimation datasets show that FinePOSE outperforms state-of-the-art methods. We further extend FinePOSE to multi-human pose estimation. Achieving 34.3mm average MPJPE on the EgoHumans dataset demonstrates the potential of FinePOSE to deal with complex multi-human scenarios. Code is available at https://***/PKU-ICST-MIPL/FinePOSE_CVPR2024.

关键词： Couplings Learning systems Three-dimensional displays Navigation Pose estimation Noise reduction Diffusion models

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R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for 3D Clothed Human Digitalization

R-Cyclic Diffuser: Reductive and Cyclic Latent Diffusion for...

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conference on computer vision and pattern recognition (CVPR)

作者： Kennard Yanting Chan Fayao Liu Guosheng Lin Chuan Sheng Foo Weisi Lin Nanyang Technological University Singapore Institute for Infocomm Research A*STAR Centre for Frontier AI Research A*STAR

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, the authors of Zero-1-to-3 demonstrated that a latent diffusion model, pretrained with Internet-scale data, can not only address the single-view 3D object reconstruction task but can even attain SOTA results in it. However, when applied to the task of single-view 3D clothed human reconstruction, Zero-1-to-3 (and related models) are unable to compete with the corresponding SOTA methods in this field despite being trained on clothed human data. In this work, we aim to tailor Zero-1-to-3's approach to the single-view 3D clothed human reconstruction task in a much more principled and structured manner. To this end, we propose R-Cyclic Diffuser, a framework that adapts Zero-1-to-3's novel approach to clothed human data by fusing it with a pixel-aligned implicit model. R-Cyclic Diffuser offers a total of three new contributions. The first and primary contribution is R-Cyclic Diffuser's cyclical conditioning mechanism for novel view synthesis. This mechanism directly addresses the view inconsistency problem faced by Zero-1-to-3 and related models. Secondly, we further enhance this mechanism with two key features - Lateral Inversion Constraint and Cyclic Noise Selection. Both features are designed to regularize and restrict the randomness of outputs generated by a latent diffusion model. Thirdly, we show how SMPL-X body priors can be incorporated in a latent diffusion model such that novel views of clothed human bodies can be generated much more accurately. Our experiments show that R-Cyclic Diffuser is able to outperform current SOTA methods in singleview 3D clothed human reconstruction both qualitatively and quantitatively. Our code is made publicly available at https://***/kcyt/r-cyclic-diffuser.

关键词： Solid modeling Adaptation models computer vision Three-dimensional displays Computational modeling Noise Diffusion models

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Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion

Behind the Veil: Enhanced Indoor 3D Scene Reconstruction wit...

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conference on computer vision and pattern recognition (CVPR)

作者： Su Sun Cheng Zhao Yuliang Guo Ruoyu Wang Xinyu Huang Yingjie Victor Chen Liu Ren Purdue University Bosch Research North America Bosch Center for Artificial Intelligence (BCAI)

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only focus on the reconstruction of the visible areas in a scene, neglecting the invisible areas due to the occlusions, e.g., the contact surface between furniture, occluded wall and floor. Our method tackles the task of completing the occluded scene surfaces, resulting in a complete 3D scene mesh. The core idea of our method is learning 3D geometry prior from various complete scenes to infer the occluded geometry of an unseen scene from solely depth measurements. We design a coarse-fine hierarchical octree representation coupled with a dual-decoder architecture, i.e., Geo-decoder and 3D Inpainter, which jointly reconstructs the complete 3D scene geometry. The Geo-decoder with detailed representation at fine levels is optimized online for each scene to reconstruct visible surfaces. The 3D Inpainter with abstract representation at coarse levels is trained offline using various scenes to complete occluded surfaces. As a result, while the Geo-decoder is specialized for an individual scene, the 3D Inpainter can be generally applied across different scenes. We evaluate the proposed method on the 3D Completed Room Scene (3D-CRS) and iTHOR datasets, significantly outperforming the SOTA methods by a gain of 16.8% and 24.2% in terms of the completeness of 3D reconstruction. 3D-CRS dataset including a complete 3D mesh of each scene is provided on project webpage 1 1 https://***/BoschRHI3NA/3D-CRS-dataset.

关键词： Geometry Surface reconstruction computer vision Three-dimensional displays Octrees Reconstruction algorithms Licenses

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TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models

TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Di...

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conference on computer vision and pattern recognition (CVPR)

作者： Haomiao Ni Bernhard Egger Suhas Lohit Anoop Cherian Ye Wang Toshiaki Koike-Akino Sharon X. Huang Tim K. Marks The Pennsylvania State University USA Friedrich- Alexander - Universität Erlangen - Nurnberg Germany Mitsubishi Electric Research Laboratories (MERL) USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., “a woman is drinking water.”). Existing TI2V frameworks often require costly training on video-text datasets and spe-cific model designs for text and image conditioning. In this paper, we propose TI2V-Zero, a zero-shot, tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input, we propose a “repeat-and-slide” strategy that modulates the reverse denoising process, al-lowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity, we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets, where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore, we show that TI2V-Zero can seam-lessly extend to other tasks such as video infilling and pre-diction when provided with more images. Its autoregressive design also supports long video generation.

关键词： Training Visualization Gaussian noise Noise reduction Diffusion models Sampling methods pattern recognition

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AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation

AiOS: All-in-One-Stage Expressive Human Pose and Shape Estim...

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conference on computer vision and pattern recognition (CVPR)

作者： Qingping Sun Yanjun Wang Ailing Zeng Wanqi Yin Chen Wei Wenjia Wang Haiyi Mei Chi-Sing Leung Ziwei Liu Lei Yang Zhongang Cai SenseTime Research City University of Hong Kong International Digital Economy Academy (IDEA) Shanghai AI Laboratory S-Lab Nanyang Technological University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body, hand, and expression estimation. Most existing methods have tack-led this task in a two-stage manner, first detecting the human body part with an off-the-shelf detection model and then in-ferring the different human body parts individually. Despite the impressive results achieved, these methods suffer from 1) loss of valuable contextual information via cropping, 2) introducing distractions, and 3) lacking inter-association among different persons and body parts, inevitably causing performance degradation, especially for crowded scenes. To address these issues, we introduce a novel ali-in-one-stage framework, AiOS, for multiple expressive human pose and shape recovery without an additional human detection step. Specifically, our method is built upon DETR, which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically, we first employ a human token to probe a hu-man location in the image and encode global features for each instance, which provides a coarse location for the later transformer block. Then, we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature, which collaborates with the global feature to regress the whole-body mesh. This straightfor-ward but effective model outperforms previous state-of-the-art methods by a 9% reduction in NMVE on AGORA, a 30% reduction in PVE on EHF, a 10% reduction in PVE on ARCTIC, and a 3% reduction in PVE on EgoBody.

关键词： Degradation computer vision Three-dimensional displays Shape Pipelines Estimation Transformers

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UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity

UFineBench: Towards Text-based Person Retrieval with Ultra-f...

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conference on computer vision and pattern recognition (CVPR)

作者： Jialong Zuo Hanyu Zhou Ying Nie Feng Zhang Tianyu Guo Nong Sang Yunhe Wang Changxin Gao National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Huawei Noah's Ark Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we con-tribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine granularity. Firstly, we construct a new dataset named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions, averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation, we also propose a spe-cial evaluation paradigm more representative of real sce-narios. It contains a new evaluation set with cross domains, cross textual granularity and cross textual styles, named UFine3C, and a new evaluation metric for accurately mea-suring retrieval ability, named mean Similarity Distribution (mSD). Moreover, we propose CFAM, a more efficient al-gorithm especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity de-coder and hard negative match mechanism. With standard in-domain evaluation, CFAM establishes competitive performance across various datasets, espe-cially on our ultra fine-grained UFine6926. Furthermore, by evaluating on UFine3C, we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at https://***/Zplusdragon/UFineBench.

关键词： Training Measurement computer vision Correlation Codes Annotations Semantics

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MPOD123: One Image to 3D Content Generation Using Mask-Enhanced Progressive Outline-to-Detail Optimization

MPOD123: One Image to 3D Content Generation Using Mask-Enhan...

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conference on computer vision and pattern recognition (CVPR)

作者： Jimin Xu Tianbao Wang Tao Jin Shengyu Zhang Dongjie Fu Zhe Wang Jiangjing Lyu Chengfei Lv Chaoyue Niu Zhou Yu Zhou Zhao Fei Wu Zhejiang University Alibaba Group Shanghai Jiaotong University Hangzbou Dianzi University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in single image driven 3D content generation have been propelled by leveraging prior knowledge from pretrained 2D diffusion models. However, the 3D content generated by existing methods often exhibits distorted outline shapes and inadequate details. To solve this problem, we propose a novel framework called Mask-enhanced Progressive Outline-to-Detail optimization (aka. MPOD123), which consists of two stages. Specifically, in the first stage, MPOD123 utilizes the pretrained view-conditioned diffusion model to guide the outline shape optimization of the 3D content. Given certain viewpoint, we estimate outline shape priors in the form of 2D mask from the 3D content by leveraging opacity calculation. In the second stage, MPOD123 incorporates Detail Appearance Inpainting (DAI) to guide the refinement on local geometry and texture with the shape priors. The essence of DAI lies in the Mask Rectified Cross-Attention (MRCA), which can be conveniently plugged in the stable diffusion model. The MRCA module utilizes the mask to rectify the attention map from each cross-attention layer. Accompanied with this new module, DAI is capable of guiding the detail refinement of the 3D content, while better preserves the outline shape. To assess the applicability in practical scenarios, we contribute a new dataset modeled on real-world e-commerce environments. Extensive quantitative and qualitative experiments on this dataset and open benchmarks demonstrate the effectiveness of MPOD123 over the state-of-the-arts.

关键词： Geometry computer vision Three-dimensional displays Shape Propulsion Benchmark testing Diffusion models

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MVBench: A Comprehensive Multi-modal Video Understanding Benchmark

MVBench: A Comprehensive Multi-modal Video Understanding Ben...

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conference on computer vision and pattern recognition (CVPR)

作者： Kunchang Li Yali Wang Yinan He Yizhuo Li Yi Wang Yi Liu Zun Wang Jilan Xu Guo Chen Ping Lou Limin Wang Yu Qiao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shanghai AI Laboratory The University of Hong Kong Pudan University State2 Key Laboratory for Novel Software Technology Nanjing University Interns at Shanghai AI Laboratory.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

With the rapid development of Multi-modal Large language Models (MLLMs), a number of diagnostic bench-marks have recently emerged to evaluate the comprehension capabilities of these models. However, most bench-marks predominantly assess spatial understanding in the static image tasks, while overlooking temporal understanding in the dynamic video tasks. To alleviate this issue, we introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench, which covers 20 chal-lenging video tasks that cannot be effectively solved with a single frame. Specifically, we first introduce a novel static-to-dynamic method to define these temporal-related tasks. By transforming various static tasks into dynamic ones, we enable the systematic generation of video tasks that require a broad spectrum of temporal skills, ranging from perception to cognition. Then, guided by the task definition, we au-tomatically convert public video annotations into multiple-choice QA to evaluate each task. On one hand, such a distinct paradigm allows us to build MVBench efficiently, without much manual intervention. On the other hand, it guarantees evaluation fairness with ground-truth video an-notations, avoiding the biased scoring of LLMs. More-over, we further develop a robust video MLLM baseline, i.e., VideoChat2, by progressive multi-modal training with di-verse instruction-tuning data. The extensive results on our MVBench reveal that, the existing MLLMs are far from sat-isfactory in temporal understanding, while our VideoChat2 largely surpasses these leading models by over 15% on MVBench. All models and data are available at https://***/OpenGVLab/Ask-Anything.

关键词： Training computer vision Systematics Large language models Image annotation Manuals Benchmark testing

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Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior

Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via C...

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conference on computer vision and pattern recognition (CVPR)

作者： Fangfu Liu Diankun Wu Yi Wei Yongming Rao Yueqi Duan Tsinghua University BAAI

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, 3D content creation from text prompts has demonstrated remarkable progress by utilizing 2D and 3D diffusion models. While 3D diffusion models ensure great multi-view consistency, their ability to generate highquality and diverse 3D assets is hindered by the limited 3D data. In contrast, 2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. However, 2D lifting methods suffer from inherent view-agnostic ambiguity thereby leading to serious multi-face Janus issues, where text prompts fail to provide sufficient guidance to learn coherent 3D results. Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement. In this paper, we propose Sherpa3D, a new text-to-3D framework that achieves high-fidelity, generalizability, and geometric consistency simultaneously. Specifically, we design a pair of guiding strategies derived from the coarse 3D prior generated by the 3D diffusion model: a structural guidance for geometric fidelity and a semantic guidance for 3D coherence. Employing the two types of guidance, the 2D diffusion model enriches the 3D content with diversified and high - quality results. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency. Project page: https://***/Sherpa3D/.

关键词： Solid modeling computer vision Three-dimensional displays Computational modeling Semantics Coherence Diffusion models

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