作者: 能夠支付 時(shí)間: 2025-3-21 22:43 作者: intoxicate 時(shí)間: 2025-3-22 01:59 作者: Pseudoephedrine 時(shí)間: 2025-3-22 08:08
Rights, Co-operation and Welfare,of 3 main components: (i)?Dense temporal upsampling between consecutive RS frames using an off-the-shelf method, (which was trained on regular video sequences), from which we extract GS “proposals”. (ii)?Learning to correctly merge an ensemble of such GS “proposals” using a dedicated MergeNet. (iii)作者: cochlea 時(shí)間: 2025-3-22 10:02
https://doi.org/10.1007/978-94-017-3294-9etworks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation.作者: LIMN 時(shí)間: 2025-3-22 13:48
Questions on the Economics of Saving,guate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the ar作者: LIMN 時(shí)間: 2025-3-22 18:34 作者: 毛細(xì)血管 時(shí)間: 2025-3-22 23:52
Fertility and Women’s Schooling in Ghanathe aligned target hair and blends both images to produce a final output. The experimental results demonstrate that our model has strengths in transferring a hairstyle under larger pose differences and preserving local hairstyle textures. The codes are available at ..作者: Induction 時(shí)間: 2025-3-23 03:55
Child Health and Mothers’ Education in Ghanator implements the information exchange, and the condition generator does not create any misalignment or pixel-squeezing artifacts. We also introduce discriminator rejection that filters out the incorrect segmentation map predictions and assures the performance of virtual try-on frameworks. Experime作者: 極大的痛苦 時(shí)間: 2025-3-23 05:39 作者: CHART 時(shí)間: 2025-3-23 12:57 作者: corpuscle 時(shí)間: 2025-3-23 14:08
https://doi.org/10.1007/978-1-349-15032-8oduces sparsity throughout training to achieve high quality even at low sample counts. After fine-tuning with the target number of samples, the resulting compact neural representation can be rendered in real-time. Our experiments demonstrate that our approach outperforms concurrent compact neural re作者: 微粒 時(shí)間: 2025-3-23 20:13
,Introduction — Divided Communities,al regions to effectively extract the features of each region for HDR image reconstruction, thereby extracting the whole information required for HDR reconstruction in the ghost regions from the entire image. Extensive experiments conducted on several benchmark datasets demonstrate the superiority o作者: GRILL 時(shí)間: 2025-3-24 00:01
,Shared Education — An Alternative Approach,the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table-based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no l作者: 慢慢流出 時(shí)間: 2025-3-24 05:22 作者: 令人心醉 時(shí)間: 2025-3-24 09:35 作者: 較早 時(shí)間: 2025-3-24 11:30 作者: 賠償 時(shí)間: 2025-3-24 16:32 作者: venous-leak 時(shí)間: 2025-3-24 22:09
,WISE: Whitebox Image Stylization by?Example-Based Learning,etworks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation.作者: CHAFE 時(shí)間: 2025-3-24 23:27 作者: exclamation 時(shí)間: 2025-3-25 07:22
,Transformers as?Meta-learners for?Implicit Neural Representations,set of INR weights with Transformers specialized as set-to-set mapping. We demonstrate the effectiveness of our method for building INRs in different tasks and domains, including 2D image regression and view synthesis for 3D objects. Our work draws connections between the Transformer hypernetworks a作者: 承認(rèn) 時(shí)間: 2025-3-25 10:15
,Style Your Hair: Latent Optimization for?Pose-Invariant Hairstyle Transfer via?Local-Style-Aware Hathe aligned target hair and blends both images to produce a final output. The experimental results demonstrate that our model has strengths in transferring a hairstyle under larger pose differences and preserving local hairstyle textures. The codes are available at ..作者: Affection 時(shí)間: 2025-3-25 13:05 作者: incite 時(shí)間: 2025-3-25 15:52
,A Codec Information Assisted Framework for?Efficient Compressed Video Super-Resolution,th Motion Vector based alignment can significantly boost the performance with negligible additional computation, even comparable to those using more complex optical flow based alignment. Secondly, by further making use of the coded video information of Residuals, the framework can be informed to ski作者: Mangle 時(shí)間: 2025-3-25 20:31 作者: 射手座 時(shí)間: 2025-3-26 03:23
,AdaNeRF: Adaptive Sampling for?Real-Time Rendering of?Neural Radiance Fields,oduces sparsity throughout training to achieve high quality even at low sample counts. After fine-tuning with the target number of samples, the resulting compact neural representation can be rendered in real-time. Our experiments demonstrate that our approach outperforms concurrent compact neural re作者: 巧辦法 時(shí)間: 2025-3-26 06:49 作者: BLUSH 時(shí)間: 2025-3-26 11:49 作者: DEBT 時(shí)間: 2025-3-26 15:41
0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19789-5978-3-031-19790-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: demote 時(shí)間: 2025-3-26 20:02
Trends in Relative World Market PricesD against additive perturbations in the latent space. Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception. We validate the effectiveness of the robustified metric through extensive experiments, showing it is more robust against manipulation. Code: 作者: 瑣事 時(shí)間: 2025-3-26 23:34
,The Journey to Work — I: Multiple Nuclei,action space. We evaluate our method on both W-Sprites and real datasets, and find that GLASS is able to generate realistic video sequences from a single input image and to successfully learn a more advanced action space than in prior work. Further details, the code and example videos are available at ..作者: compel 時(shí)間: 2025-3-27 03:20 作者: Incompetent 時(shí)間: 2025-3-27 06:13 作者: 字形刻痕 時(shí)間: 2025-3-27 11:27 作者: 配偶 時(shí)間: 2025-3-27 15:21
,Controllable Video Generation Through Global and?Local Motion Dynamics,action space. We evaluate our method on both W-Sprites and real datasets, and find that GLASS is able to generate realistic video sequences from a single input image and to successfully learn a more advanced action space than in prior work. Further details, the code and example videos are available at ..作者: 你敢命令 時(shí)間: 2025-3-27 17:47 作者: AUGER 時(shí)間: 2025-3-28 01:53
,Improving the?Perceptual Quality of?2D Animation Interpolation,ar Discrepancy metric (RRLD) to automate the previously manual training data collection process. Lastly, we explore evaluation of 2D animation generation through a user study, and establish that the LPIPS perceptual metric and chamfer line distance (CD) are more appropriate measures of quality than PSNR and SSIM used in prior art.作者: 過(guò)份好問(wèn) 時(shí)間: 2025-3-28 03:57 作者: epidermis 時(shí)間: 2025-3-28 09:29 作者: Organization 時(shí)間: 2025-3-28 13:53
Rights, Co-operation and Welfare, Sky Time-lapse, and Taichi-HD datasets can generate diverse, coherent, and high-quality long videos. We also showcase conditional extensions of our approach for generating meaningful long videos by incorporating temporal information with text and audio. Videos and code can be found at ..作者: Spangle 時(shí)間: 2025-3-28 17:56
,Long Video Generation with?Time-Agnostic VQGAN and?Time-Sensitive Transformer, Sky Time-lapse, and Taichi-HD datasets can generate diverse, coherent, and high-quality long videos. We also showcase conditional extensions of our approach for generating meaningful long videos by incorporating temporal information with text and audio. Videos and code can be found at ..作者: 蝕刻 時(shí)間: 2025-3-28 20:18 作者: ABIDE 時(shí)間: 2025-3-29 00:35
,Editing Out-of-Domain GAN Inversion via?Differential Activations,construction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framew作者: 跟隨 時(shí)間: 2025-3-29 05:44 作者: crockery 時(shí)間: 2025-3-29 07:14 作者: BAN 時(shí)間: 2025-3-29 12:55
,Inpainting at?Modern Camera Resolution by?Guided PatchMatch with?Auto-curation,ern cameras such as 4K or more, and for large holes. We contribute an inpainting benchmark dataset of photos at 4K and above representative of modern sensors. We demonstrate a novel framework that combines deep learning and traditional methods. We use an existing deep inpainting model LaMa?[.] to fi作者: 保留 時(shí)間: 2025-3-29 17:47 作者: GUEER 時(shí)間: 2025-3-29 22:47 作者: Exhilarate 時(shí)間: 2025-3-30 00:52 作者: Fracture 時(shí)間: 2025-3-30 06:23 作者: arrhythmic 時(shí)間: 2025-3-30 10:43 作者: POLYP 時(shí)間: 2025-3-30 14:18
,Neural Radiance Transfer Fields for?Relightable Novel-View Synthesis with?Global Illumination,omputer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics too作者: Simulate 時(shí)間: 2025-3-30 19:17
,Transformers as?Meta-learners for?Implicit Neural Representations, the given observations usually requires optimization with gradient descent from scratch, which is inefficient and does not generalize well with sparse observations. To address this problem, most of the prior works train a hypernetwork that generates a single vector to modulate the INR weights, wher作者: 完整 時(shí)間: 2025-3-30 21:33 作者: 歡笑 時(shí)間: 2025-3-31 04:15 作者: fulcrum 時(shí)間: 2025-3-31 08:37 作者: 證明無(wú)罪 時(shí)間: 2025-3-31 11:50
,Injecting 3D Perception of?Controllable NeRF-GAN into?StyleGAN for?Editable Portrait Image Synthesicess, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work,作者: Stable-Angina 時(shí)間: 2025-3-31 16:23 作者: progestogen 時(shí)間: 2025-3-31 20:32
,Improving the?Perceptual Quality of?2D Animation Interpolation,rame interpolation may ease this burden, 2D animation poses additional difficulties compared to photorealistic video. In this work, we address challenges unexplored in previous animation interpolation systems, with a focus on improving perceptual quality. Firstly, we propose SoftsplatLite (SSL), a f作者: Valves 時(shí)間: 2025-3-31 22:00
Selective TransHDR: Transformer-Based Selective HDR Imaging Using Ghost Region Mask, In the weakly misaligned region, ghost artifacts can be suppressed using convolutional neural network (CNN)-based methods. However, in highly misaligned regions, it is necessary to extract features from the global region because the necessary information does not exist in the local region. Therefor作者: CAMP 時(shí)間: 2025-4-1 05:28
,Learning Series-Parallel Lookup Tables for?Efficient Image Super-Resolution,dence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this 作者: 直覺(jué)沒(méi)有 時(shí)間: 2025-4-1 09:01
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234260.jpg作者: Bravura 時(shí)間: 2025-4-1 12:26
https://doi.org/10.1007/978-3-031-19790-1artificial intelligence; computer networks; computer security; computer vision; correlation analysis; fac作者: GLUE 時(shí)間: 2025-4-1 16:04 作者: alcohol-abuse 時(shí)間: 2025-4-1 20:27
Computer Vision – ECCV 2022978-3-031-19790-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
