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GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields

GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fie...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Alessandro Ruzzi Xiangwei Shi Xi Wang Gengyan Li Shalini De Mello Hyung Jin Chang Xucong Zhang Otmar Hilliges Department of Computer Science ETH Zürich Computer Vision Lab Delft University of Technology NVIDIA School of Computer Science University of Birmingham

We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-stream architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation. Code and models will be released for research purposes.

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Pose recognition with Cascade Transformers

Pose Recognition with Cascade Transformers

引用

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

作者： Li, Ke Wang, Shijie Zhang, Xiang Xu, Yifan Xu, Weijian Tu, Zhuowen Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

关键词： Heating systems Visualization computer vision Transformers pattern recognition Decoding Task analysis

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Image Super-Resolution with Non-Local Sparse Attention

Image Super-Resolution with Non-Local Sparse Attention

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

作者： Mei, Yiqun Fan, Yuchen Zhou, Yuqian Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665445092

Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL operation while enjoying robustness and high-efficiency of sparse representation. Specifically, NLSA rectifies non-local attention with spherical locality sensitive hashing (LSH) that partitions the input space into hash buckets of related features. For every query signal, NLSA assigns a bucket to it and only computes attention within the bucket. The resulting sparse attention prevents the model from attending to locations that are noisy and less-informative, while reducing the computational cost from quadratic to asymptotic linear with respect to the spatial size. Extensive experiments validate the effectiveness and efficiency of NLSA. With a few non-local sparse attention modules, our architecture, called non-local sparse network (NLSN), reaches state-of-the-art performance for SISR quantitatively and qualitatively.

关键词： computer vision Computational modeling Superresolution computer architecture Benchmark testing Robustness pattern recognition

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Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis

Quantitative Manipulation of Custom Attributes on 3D-Aware I...

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conference on computer vision and pattern recognition (cvpr)

作者： Hoseok Do EunKyung Yoo Taehyeong Kim Chul Lee Jin young Choi AI Lab CTO Division LG Electronics South Korea Dept. of Electrical and Computer Engineering ASRI Seoul National University South Korea Dept. of Biosystems Engineering Seoul National University South Korea

While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such research gap, we propose a novel image manipulation model of 3D-based GAN representations for a fine-grained control of specific custom attributes. By extending the latest 3D-based GAN models (e.g., EG3D), our user-friendly quantitative manipulation model enables a fine yet normalized control of 3D manipulation of multi-attribute quantities while achieving view consistency. We validate the effectiveness of our proposed technique both qualitatively and quantitatively through various experiments.

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DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields

DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance F...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yu Chen Gim Hee Lee Department of Computer Science National University of Singapore

Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs. Despite the impressive results, these methods cannot be applied to Generalizable NeRFs (GeNeRFs) which require image feature extractions that are often based on more complicated 3D CNN or transformer architectures. In this work, we first analyze the difficulties of jointly optimizing camera poses with GeNeRFs, and then further propose our DBARF to tackle these issues. Our DBARF which bundle adjusts camera poses by taking a cost feature map as an implicit cost function can be jointly trained with GeNeRFs in a self-supervised manner. Unlike BARF and its follow-up works, which can only be applied to per-scene optimized NeRFs and need accurate initial camera poses with the exception of forward-facing scenes, our method can generalize across scenes and does not require any good initialization. Experiments show the effectiveness and generalization ability of our DBARF when evaluated on real-world datasets. Our code is available at https://***/DBARF.

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NeighborTrack: Single Object Tracking by Bipartite Matching with Neighbor Tracklets and Its Applications to Sports

NeighborTrack: Single Object Tracking by Bipartite Matching ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Chen, Yu-Hsi Wang, Chien-Yao Yang, Cheng-Yun Chang, Hung-Shuo Lin, Youn-Long Chuang, Yung-Yu Liao, Hong-Yuan Mark Institute of Information Science Academia Sinica Taiwan Purdue University Department of Electrical Engineering United States National Tsing Hua University Department of Computer Science Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350302493

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this information to improve the tracking results. When tracking an occluded target, its appearance features are untrustworthy. However, a general siamese network often cannot tell whether the tracked object is occluded by reading the confidence score alone, because it could be misled by neighbors with high confidence scores. Our proposed NeighborTrack takes advantage of unoccluded neighbors' information to reconfirm the tracking target and reduces false tracking when the target is occluded. For the VOT challenge dataset commonly used in short-term object tracking, we improve three famous SOT networks, Ocean, TransT, and OSTrack, by an average of 1.92% EAO and 2.11% robustness. For the mid- and long-term tracking experiments based on OSTrack, we achieve state-of-the-art 72.25% AUC on LaSOT and 75.7% AO on GOT-10K. Most of the tracking examples we have used are related to sports. © 2023 ieee.

关键词： Target tracking

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Generating Anomalies for Video Anomaly Detection with Prompt-based Feature Mapping

Generating Anomalies for Video Anomaly Detection with Prompt...

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conference on computer vision and pattern recognition (cvpr)

作者： Zuhao Liu Xiao-Ming Wu Dian Zheng Kun-Yu Lin Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Anomaly detection in surveillance videos is a challenging computer vision task where only normal videos are available during training. Recent work released the first virtual anomaly detection dataset to assist real-world detection. However, an anomaly gap exists because the anomalies are bounded in the virtual dataset but unbounded in the real world, so it reduces the generalization ability of the virtual dataset. There also exists a scene gap between virtual and real scenarios, including scene-specific anomalies (events that are abnormal in one scene but normal in another) and scene-specific attributes, such as the viewpoint of the surveillance camera. In this paper, we aim to solve the problem of the anomaly gap and scene gap by proposing a prompt-based feature mapping framework (PFMF). The PFMF contains a mapping network guided by an anomaly prompt to generate unseen anomalies with unbounded types in the real scenario, and a mapping adaptation branch to narrow the scene gap by applying domain classifier and anomaly classifier. The proposed framework outperforms the state-of-the-art on three benchmark datasets. Extensive ablation experiments also show the effectiveness of our framework design.

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

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2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 ieee.

关键词： Intensive care units

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StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-Based Generator

StyleSync: High-Fidelity Generalized and Personalized Lip Sy...

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conference on computer vision and pattern recognition (cvpr)

作者： Jiazhi Guan Zhanwang Zhang Hang Zhou Tianshu Hu Kaisiyuan Wang Dongliang He Haocheng Feng Jingtuo Liu Errui Ding Ziwei Liu Jingdong Wang Department of Computer Vision Technology (VIS) Baidu Inc. Tsinghua University The University of Sydney S-Lab Nanyang Technological University

Despite recent advances in syncing lip movements with any audio waves, current methods still struggle to balance generation quality and the model's generalization ability. Previous studies either require long-term data for training or produce a similar movement pattern on all subjects with low quality. In this paper, we propose StyleSync, an effective framework that enables high-fidelity lip synchronization. We identify that a style-based generator would sufficiently enable such a charming property on both one-shot and few-shot scenarios. Specifically, we design a mask-guided spatial information encoding module that preserves the details of the given face. The mouth shapes are accurately modified by audio through modulated convolutions. Moreover, our design also enables personalized lip-sync by introducing style space and generator refinement on only limited frames. Thus the identity and talking style of a target person could be accurately preserved. Extensive experiments demonstrate the effectiveness of our method in producing high-fidelity results on a variety of scenes. Resources can be found at https:/***/projects/StyleSync.

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VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction

VolRecon: Volume Rendering of Signed Ray Distance Functions ...

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conference on computer vision and pattern recognition (cvpr)

作者： Yufan Ren Fangjinhua Wang Tong Zhang Marc Pollefeys Sabine Süsstrunk IVRL IC EPFL Department of Computer Science ETH Zurich

The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural implicit reconstruction methods optimize perscene parameters and therefore lack generalizability to new scenes. We introduce VolRecon, a novel generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct the scene with fine details and little noise, VolRecon combines projection features aggregated from multi-view features, and volume features interpolated from a coarse global feature volume. Using a ray transformer, we compute SRDF values of sampled points on a ray and then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable accuracy as MVSNet in full view reconstruction. Furthermore, our approach exhibits good generalization performance on the large-scale ETH3D benchmark. Code is available at https://***/IVRL/VolRecon/.

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