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Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

Modernizing Old Photos Using Multiple References via Photore...

引用

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

作者： Gunawan, Agus Kim, Soo Ye Sim, Hyeonjun Lee, Jae-Ho Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Francisco CA USA ETRI Daejeon South Korea Qualcomm San Diego CA USA

ISBN: (纸本)9798350301298

This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.

关键词： Low-level vision

来源：评论

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Structured Sparsity Learning for Efficient Video Super-Resolution

Structured Sparsity Learning for Efficient Video Super-Resol...

引用

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

作者： Xia, Bin He, Jingwen Zhang, Yulun Wang, Yitong Tian, Yapeng Yang, Wenming Van Gool, Luc Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Swiss Fed Inst Technol Zurich Switzerland ByteDance Inc Beijing Peoples R China Univ Texas Dallas Dallas TX USA

ISBN: (纸本)9798350301298

The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, e.g., smartphones and drones. Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. The code is available at https://***/Zj-BinXia/SSL.

关键词： Low-level vision

来源：评论

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Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging

Residual Degradation Learning Unfolding Framework with Mixin...

引用

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

作者： Dong, Yubo Gao, Dahua Qiu, Tian Li, Yuyan Yang, Minxi Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350301298

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion;in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a MixS2 Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the MixS2 Transformer into the RDLUF leads to an end-to-end trainable neural network RDLUF-MixS2. Experimental results establish the superior performance of the proposed method over existing ones. Code is available: https: //***/ShawnDong98/RDLUF_MixS2

关键词： Low-level vision

来源：评论

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PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering

PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast...

引用

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

作者： Yan, Han Liu, Celong Ma, Chao Mei, Xing Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Shanghai Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching. Our method, Plenoptic VDB (PlenVDB), directly learns the VDB data structure from a set of posed images by means of a novel training strategy and then uses it for real-time rendering. Experimental results demonstrate the effectiveness and the efficiency of our method over previous arts: First, it converges faster in the training process. Second, it delivers a more compact data format for NeRF data presentation. Finally, it renders more efficiently on commodity graphics hardware. Our mobile PlenVDB demo achieves 30+ FPS, 1280x720 resolution on an iPhone12 mobile phone. Check *** for details.

关键词： vision + graphics

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Transfer4D: A framework for frugal motion capture and deformation transfer

Transfer4D: A framework for frugal motion capture and deform...

引用

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

作者： Maheshwari, Shubh Narain, Rahul Hebbalaguppe, Ramya TCS Res Gurugram India Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9798350301298

Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a frugal alternative termed "Transfer4D" that uses only commodity depth sensors and further reduces animators' effort by automating the rigging and animation transfer process. Our approach can transfer motion from an incomplete, single-view depth video to a semantically similar target mesh, unlike prior works that make a stricter assumption on the source to be noise-free and watertight. To handle sparse, incomplete videos from depth video inputs and variations between source and target objects, we propose to use skeletons as an intermediary representation between motion capture and transfer. We propose a novel unsupervised skeleton extraction pipeline from a single-view depth sequence that incorporates additional geometric information, resulting in superior performance in motion reconstruction and transfer in comparison to the contemporary methods and making our approach generic. We use non-rigid reconstruction to track motion from the depth sequence, and then we rig the source object using skinning decomposition. Finally, the rig is embedded into the target object for motion retargeting.

关键词： vision + graphics

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DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation

DualVector: Unsupervised Vector Font Synthesis with Dual-Par...

引用

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

作者： Liu, Ying-Tian Zhang, Zhifei Guo, Yuan-Chen Fisher, Matthew Wang, Zhaowen Zhang, Song-Hai Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization. On the other hand, existing vector font synthesis methods either fail to represent the shape concisely or require vector supervision during training. To push the quality of vector font synthesis to the next level, we propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs. The glyph contour is then obtained by boolean operations on these paths. We first learn such a representation only from glyph images and devise a subsequent contour refinement step to align the contour with an image representation to further enhance details. Our method, named DualVector, outperforms state-of-the-art methods in vector font synthesis both quantitatively and qualitatively. Our synthesized vector fonts can be easily converted to common digital font formats like TrueType Font for practical use. The code is released at https://***/thuliu-yt16/dualvector.

关键词： vision + graphics

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VILA: Learning Image Aesthetics from User Comments with vision-Language Pretraining

VILA: Learning Image Aesthetics from User Comments with Visi...

引用

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

作者： Ke, Junjie Ye, Keren Yu, Jiahui Wu, Yonghui Milanfar, Peyman Yang, Feng Google Res New York NY 10019 USA

ISBN: (纸本)9798350301298

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal fine-tuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.(1)

关键词： Computational imaging

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Learning to Zoom and Unzoom

Learning to Zoom and Unzoom

引用

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

作者： Thavamani, Chittesh Li, Mengtian Ferroni, Francesco Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA Waymo Mountain View CA USA Nvidia Santa Clara CA USA

ISBN: (纸本)9798350301298

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well. Code and additional visuals are available at https://***/lzu/.

关键词： Efficient and scalable vision

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

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

作者： Memmel, Marius Bachmann, Roman Zamir, Amir Univ Washington Seattle WA 98195 USA Swiss Fed Inst Technol EPFL Lausanne Switzerland

ISBN: (纸本)9798350301298

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

关键词： Embodied vision: Active agents simulation

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PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

PyramidFlow: High-Resolution Defect Contrastive Localization...

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

作者： Lei, Jiarui Hu, Xiaobo Wang, Yue Liu, Dong Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou Peoples R China Jiaxing Key Lab Photon Sensing & Intelligent Imag Jiaxing Peoples R China Zhejiang Univ Jiaxing Res Inst Intelligent Opt & Photon Res Ctr Hangzhou Peoples R China

ISBN: (纸本)9798350301298

During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in low-resolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

关键词： Low-level vision

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