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Pointersect: Neural Rendering with Cloud-Ray Intersection

Pointersect: Neural Rendering with Cloud-Ray Intersection

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chang, Jen-Hao Rick Chen, Wei -Yu Raujan, Anurag Yi, Kwang Moo Tuzel, Oncel Apple Cupertino CA 95014 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ British Columbia Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9798350301298

We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global illumination. Unlike existing work that focuses on converting point clouds to other representations-e.g., surfaces or implicit functions-our key idea is to directly infer the intersection of a light ray with the underlying surface represented by the given point cloud. Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray. Localizing the problem into small neighborhoods enables us to train a model with only 48 meshes and apply it to unseen point clouds. Our model achieves higher estimation accuracy than state-of-the-art surface reconstruction and point-cloud rendering methods on three test sets. When applied to room-scale point clouds, without any scene-specific optimization, the model achieves competitive quality with the state-of-the-art novel-view rendering methods. Moreover, we demonstrate ability to render and manipulate Lidar-scanned point clouds such as lighting control and object insertion.

关键词： vision + graphics

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Recognizability Embedding Enhancement for Very Low-Resolution Face recognition and Quality Estimation

Recognizability Embedding Enhancement for Very Low-Resolutio...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chai, Jacky Chen Long Ng, Tiong-Sik Low, Cheng-Yaw Park, Jaewoo Teoh, Andrew Beng Jin Yonsei Univ Seoul South Korea Inst for Basic Sci Korea Daejeon South Korea

ISBN: (纸本)9798350301298

Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.

关键词： Biometrics

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PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

PosterLayout: A New Benchmark and Approach for Content-aware...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hsu, Hsiao Yuan He, Xiangteng Peng, Yuxin Kong, Hao Zhang, Qing Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798350301298

Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PKU PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases. The dataset and the source code are available at https://***/PKU-ICSTMIPL/PosterLayout-cvpr2023.

关键词： vision applications and systems

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PROB: Probabilistic Objectness for Open World Object Detection

PROB: Probabilistic Objectness for Open World Object Detecti...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zohar, Orr Wang, Kuan-Chieh Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350301298

Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection (similar to 2x unknown recall) and known object detection (similar to 10% mAP). Our code is available at https://***/orrzohar/PROB.

关键词： detection recognition: Categorization retrieval

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Compression-Aware Video Super-Resolution

Compression-Aware Video Super-Resolution

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Yingwei Isobe, Takashi Jia, Xu Tao, Xin Lu, Huchuan Tai, Yu-Wing Dalian Univ Technol Dalian Peoples R China Xiaohongshu Inc Shanghai Peoples R China Kuaishou Technol Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications. In spite of a few pioneering works being proposed recently to super-resolve the compressed videos, they are not specially designed to deal with videos of various levels of compression. In this paper, we propose a novel and practical compression-aware video super-resolution model, which could adapt its video enhancement process to the estimated compression level. A compression encoder is designed to model compression levels of input frames, and a base VSR model is then conditioned on the implicitly computed representation by inserting compression-aware modules. In addition, we propose to further strengthen the VSR model by taking full advantage of meta data that is embedded naturally in compressed video streams in the procedure of information fusion. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method on compressed VSR benchmarks. The codes will be available at https://***/aprBlue/CAVSR

关键词： Low-level vision

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Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

Gated Multi-Resolution Transfer Network for Burst Restoratio...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mehta, Nancy Dudhane, Akshay Murala, Subrahmanyam Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Indian Inst Technol Ropar CVPR Lab Rupnagar India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350301298

Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMT-Net consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validate our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. Our codes and models are available at https://***/nanmehta/GMTNet.

关键词： Low-level vision

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OCTET: Object-aware Counterfactual Explanations

OCTET: Object-aware Counterfactual Explanations

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zemni, Mehdi Chen, Mickael Zablocki, Eloi Ben-Younes, Hedi Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798350301298

Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image that would also change the output of the model to be explained. Such explanations point end-users at the main factors that impact the decision of the model. However, previous methods struggle to explain decision models trained on images with many objects, e.g., urban scenes, which are more difficult to work with but also arguably more critical to explain. In this work, we propose to tackle this issue with an object-centric framework for counterfactual explanation generation. Our method, inspired by recent generative modeling works, encodes the query image into a latent space that is structured in a way to ease object-level manipulations. Doing so, it provides the end-user with control over which search directions (e.g., spatial displacement of objects, style modification, etc.) are to be explored during the counterfactual generation. We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e.g., to explain semantic segmentation models. To complete our analysis, we design and run a user study that measures the usefulness of counterfactual explanations in understanding a decision model. Code is available at https://***/valeoai/OCTET.

关键词： Explainable computer vision

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HOICLIP: Efficient Knowledge Transfer for HOI Detection with vision-Language Models

HOICLIP: Efficient Knowledge Transfer for HOI Detection with...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ning, Shan Qiu, Longtian Liu, Yongfei He, Xuming ShanghaiTech Univ Shanghai Peoples R China ByteDance Inc Beijing Peoples R China Shanghai Engn Res Ctr Intelligent Vis & 1maging Shanghai Peoples R China

ISBN: (纸本)9798350301298

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human-object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://***/Artanic30/HOICLIP.

关键词： detection recognition: Categorization retrieval

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Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation

Orientation-conditioned Facial Texture Mapping for Video-bas...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cantrill, Sam Ahmedt-Aristizabal, David Petersson, Lars Suominen, Hanna Armin, Mohammad Ali Australian Natl Univ Canberra ACT Australia Commonwealth & Sci Ind Res Org Data61 Canberra ACT Australia Univ Turku Turku Finland

ISBN: (纸本)9798350365474

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this study, we leverage the 3D facial surface to construct a novel orientation-conditioned facial texture video representation which improves the motion robustness of existing video-based facial rPPG estimation methods. Our proposed method achieves a significant 18.2% performance improvement in cross-dataset testing on MMPD over our baseline using the PhysNet model trained on PURE, highlighting the efficacy and generalization benefits of our designed video representation. We demonstrate significant performance improvements of up to 29.6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion. Emphasizing the benefits the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation. We validate the efficacy of our design decisions and the impact of different video processing steps through an ablation study. Our findings illustrate the potential strengths of exploiting the 3D facial surface as a general strategy for addressing dynamic and unconstrained subject motion in videos. The code is available at https://***/orientation-uv-rppg/.

关键词： computer vision motion rppg

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Rethinking the Learning Paradigm for Dynamic Facial Expression recognition

Rethinking the Learning Paradigm for Dynamic Facial Expressi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Hanyang Li, Bo Wu, Shuang Shen, Siyuan Liu, Feng Ding, Shouhong Zhou, Aimin East China Normal Univ Shanghai Inst AI Educ Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Tencent Youtu Lab Beijing Peoples R China East China Normal Univ Shanghai Int Sch Chief Technol Officer Shanghai Peoples R China

ISBN: (纸本)9798350301298

Dynamic Facial Expression recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format. Previous research has considered non-target frames as noisy frames, but we propose that it should be treated as a weakly supervised problem. We also identify the imbalance of short- and long-term temporal relationships in DFER. Therefore, we introduce the Multi-3D Dynamic Facial Expression Learning (M3DFEL) framework, which utilizes Multi-Instance Learning (MIL) to handle inexact labels. M3DFEL generates 3D-instances to model the strong short-term temporal relationship and utilizes 3DCNNs for feature extraction. The Dynamic Long-term Instance Aggregation Module (DLIAM) is then utilized to learn the long-term temporal relationships and dynamically aggregate the instances. Our experiments on DFEW and FERV39K datasets show that M3DFEL outperforms existing state-of-the-art approaches with a vanilla R3D18 backbone. The source code is available at https://***/faceeyes/M3DFEL.

关键词： body gesture Humans: Face movement pose

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