作者: 欄桿 時(shí)間: 2025-3-21 21:07 作者: dictator 時(shí)間: 2025-3-22 01:50 作者: SHRIK 時(shí)間: 2025-3-22 05:41
,Freditor: High-Fidelity and?Transferable NeRF Editing by?Frequency Decomposition,D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more multiview-consistent after editing compared with their high-frequency parts. Moreover, the a作者: Engaging 時(shí)間: 2025-3-22 09:56
,DoughNet: A Visual Predictive Model for?Topological Manipulation of?Deformable Objects,hese topological changes that a specific action might incur is critical for planning interactions with elastoplastic objects. We present DoughNet, a Transformer-based architecture for handling these challenges, consisting of two components. First, a denoising autoencoder represents deformable object作者: 劇毒 時(shí)間: 2025-3-22 15:28
,PAV: Personalized Head Avatar from?Unstructured Video Collection,hat learns a dynamic deformable neural radiance field (NeRF), in particular from a collection of monocular talking face videos of the same character under various appearance and shape changes. Unlike existing head NeRF methods that are limited to modeling such input videos on a per-appearance basis,作者: 劇毒 時(shí)間: 2025-3-22 21:05 作者: FACET 時(shí)間: 2025-3-22 21:53 作者: DUCE 時(shí)間: 2025-3-23 02:14
,MultiDelete for?Multimodal Machine Unlearning,s purging private, inaccurate, or outdated information from trained models without the need for complete re-training. Unlearning within a multimodal setting presents unique challenges due to the complex dependencies between different data modalities and the expensive cost of training on large multim作者: minion 時(shí)間: 2025-3-23 07:36
,Unified Local-Cloud Decision-Making via?Reinforcement Learning,straints to optimize operation across dynamic tasks and contexts. As local computation tends to be restricted, offloading the computation, .., to a remote server, can save local resources while providing access to high-quality predictions from powerful and large models. However, the resulting commun作者: 宇宙你 時(shí)間: 2025-3-23 13:42
,UniTalker: Scaling up?Audio-Driven 3D Facial Animation Through A Unified Model, 3D annotations, restricting previous models to training on specific annotations and thereby constraining the training scale. In this work, we present ., a unified model featuring a multi-head architecture designed to effectively leverage datasets with varied annotations. To enhance training stabili作者: 嚙齒動物 時(shí)間: 2025-3-23 14:11
Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulbeings, this ability is rooted in the understanding of semantic correspondence among different objects, which helps to naturally transfer the interaction experience of familiar objects to novel ones. Although robots lack such a reservoir of interaction experience, the vast availability of human vide作者: 過剩 時(shí)間: 2025-3-23 21:40 作者: 越自我 時(shí)間: 2025-3-24 00:36
Stitched ViTs are Flexible Vision Backbones,iTs are inefficient in terms of training and deployment, because adopting ViTs with individual sizes requires separate trainings and is restricted by fixed performance-efficiency trade-offs. In this paper, we are inspired by stitchable neural networks (SN-Net), which is a new framework that cheaply 作者: Ankylo- 時(shí)間: 2025-3-24 04:59
TrajPrompt: Aligning Color Trajectory with Vision-Language Representations, alignment between different data sources, the external modality cannot fully exhibit its value. For example, recent trajectory prediction approaches incorporate the Bird’s-Eye-View (BEV) scene as an additional source but do not significantly improve performance compared to single-source strategies,作者: 繁榮地區(qū) 時(shí)間: 2025-3-24 09:47 作者: 暫時(shí)別動 時(shí)間: 2025-3-24 12:17 作者: CANT 時(shí)間: 2025-3-24 16:02 作者: 顛簸下上 時(shí)間: 2025-3-24 21:42 作者: Monocle 時(shí)間: 2025-3-25 00:15 作者: DALLY 時(shí)間: 2025-3-25 07:18 作者: 單調(diào)女 時(shí)間: 2025-3-25 07:29
https://doi.org/10.1007/978-3-663-02301-2quency feature space, enabling stable intensity control and novel scene transfer. Comprehensive experiments conducted on photorealistic datasets demonstrate the superior performance of high-fidelity and transferable NeRF editing. The project page is at ..作者: Amenable 時(shí)間: 2025-3-25 12:15 作者: enlist 時(shí)間: 2025-3-25 16:31
Dermato-venerologische Untersuchungen,mance on five public datasets: Pascal VOC, Pascal Context, COCO-object, COCO-stuff, and ADE 20K. Especially, the visually appealing segmentation results demonstrate the model capability to localize objects precisely.作者: sulcus 時(shí)間: 2025-3-25 21:37 作者: paltry 時(shí)間: 2025-3-26 02:13 作者: Limited 時(shí)間: 2025-3-26 05:20 作者: 知識分子 時(shí)間: 2025-3-26 09:33
,In Defense of?Lazy Visual Grounding for?Open-Vocabulary Semantic Segmentation,mance on five public datasets: Pascal VOC, Pascal Context, COCO-object, COCO-stuff, and ADE 20K. Especially, the visually appealing segmentation results demonstrate the model capability to localize objects precisely.作者: 元音 時(shí)間: 2025-3-26 14:05
Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinf作者: 下垂 時(shí)間: 2025-3-26 17:38
0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers deal with topics such as computer vision; machine learning; deep neural netwo作者: Androgen 時(shí)間: 2025-3-26 22:29 作者: 堅(jiān)毅 時(shí)間: 2025-3-27 03:27
Aufbau des Betriebssystems der Unternehmunged from a casually captured video only support body motions without facial expressions and hand motions. In this work, we present ., an expressive whole-body 3D human avatar learned from a short monocular video. We design ExAvatar as a combination of the whole-body parametric mesh model (SMPL-X) and作者: 陪審團(tuán)每個(gè)人 時(shí)間: 2025-3-27 09:05 作者: 富饒 時(shí)間: 2025-3-27 10:24
https://doi.org/10.1007/978-3-663-02301-2D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more multiview-consistent after editing compared with their high-frequency parts. Moreover, the a作者: assent 時(shí)間: 2025-3-27 14:31 作者: 極少 時(shí)間: 2025-3-27 19:19
Werner Sauter,Hermann-Josef Wolfhat learns a dynamic deformable neural radiance field (NeRF), in particular from a collection of monocular talking face videos of the same character under various appearance and shape changes. Unlike existing head NeRF methods that are limited to modeling such input videos on a per-appearance basis,作者: 保留 時(shí)間: 2025-3-27 23:47
Organisation des Leistungsprozesses,stillation to balance the stability of existing knowledge with the adaptability to new information. This technique retraces the features associated with past classes based on the final label assignment results, performing knowledge distillation targeting these specific features from the previous mod作者: MORT 時(shí)間: 2025-3-28 02:47
Dermato-venerologische Untersuchungen,Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classification capability of pretrained vision-and-language models. We argue that visual objects are distinguishable without the prior text information as segme作者: 闡明 時(shí)間: 2025-3-28 09:36 作者: 牽連 時(shí)間: 2025-3-28 14:13
https://doi.org/10.1007/978-3-642-77398-3straints to optimize operation across dynamic tasks and contexts. As local computation tends to be restricted, offloading the computation, .., to a remote server, can save local resources while providing access to high-quality predictions from powerful and large models. However, the resulting commun作者: Classify 時(shí)間: 2025-3-28 17:18 作者: antenna 時(shí)間: 2025-3-28 18:56 作者: Culpable 時(shí)間: 2025-3-29 01:27 作者: LUMEN 時(shí)間: 2025-3-29 04:48 作者: 金盤是高原 時(shí)間: 2025-3-29 11:15
https://doi.org/10.1007/978-3-642-90911-5 alignment between different data sources, the external modality cannot fully exhibit its value. For example, recent trajectory prediction approaches incorporate the Bird’s-Eye-View (BEV) scene as an additional source but do not significantly improve performance compared to single-source strategies,作者: 單調(diào)女 時(shí)間: 2025-3-29 13:06
Die Eingriffe in der Speiser?hreeen point clouds, suffers from redundant feature interactions among semantically unrelated regions. Additionally, recent methods rely only on 3D information to extract robust feature representations, while overlooking the rich semantic information in 2D images. In this paper, we propose SemReg, a no作者: Colonoscopy 時(shí)間: 2025-3-29 18:08
Elektrochirurgisches InstrumentariumD latent diffusion model to the 3D scope. The target-view image is generated with a single-view source image and the camera pose as condition information. However, due to the high sparsity of the single input image, Zero-1-to-3 tends to produce geometry and appearance inconsistency across views, esp作者: 項(xiàng)目 時(shí)間: 2025-3-29 20:18 作者: PTCA635 時(shí)間: 2025-3-30 02:01 作者: 充滿裝飾 時(shí)間: 2025-3-30 04:37
https://doi.org/10.1007/978-3-031-72940-9artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 國家明智 時(shí)間: 2025-3-30 08:45 作者: 出來 時(shí)間: 2025-3-30 15:07
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242320.jpg作者: 從容 時(shí)間: 2025-3-30 19:57
Expressive Whole-Body 3D Gaussian Avatar,noticeable artifacts under novel motions. To address them, we introduce our hybrid representation of the mesh and 3D Gaussians. Our hybrid representation treats each 3D Gaussian as a vertex on the surface with pre-defined connectivity information (., triangle faces) between them following the mesh t作者: 有權(quán) 時(shí)間: 2025-3-30 22:48
Controllable Human-Object Interaction Synthesis, contact. To overcome these problems, we introduce an object geometry loss as additional supervision to improve the matching between generated object motion and input object waypoints; we also design guidance terms to enforce contact constraints during the sampling process of the trained diffusion m作者: 微枝末節(jié) 時(shí)間: 2025-3-31 04:52
,PAV: Personalized Head Avatar from?Unstructured Video Collection,NeRF framework to model appearance and shape variations in a single unified network for multi-appearances of the same subject. We demonstrate experimentally that PAV outperforms the baseline method in terms of visual rendering quality in our quantitative and qualitative studies on various subjects.作者: 上下倒置 時(shí)間: 2025-3-31 08:55
,Strike a?Balance in?Continual Panoptic Segmentation,nnotated only for the classes of their original step, we devise balanced anti-misguidance losses, which combat the impact of incomplete annotations without incurring classification bias. Building upon these innovations, we present a new method named Balanced Continual Panoptic Segmentation (BalConpa作者: 稱贊 時(shí)間: 2025-3-31 11:34 作者: dandruff 時(shí)間: 2025-3-31 15:41 作者: critique 時(shí)間: 2025-3-31 20:37
,UniTalker: Scaling up?Audio-Driven 3D Facial Animation Through A Unified Model,, typically less than 1?h, to 18.5?h. With a single trained UniTalker model, we achieve substantial lip vertex error reductions of 9.2% for BIWI dataset and 13.7% for Vocaset. Additionally, the pre-trained UniTalker exhibits promise as the foundation model for audio-driven facial animation tasks. Fi作者: 釘牢 時(shí)間: 2025-3-31 21:51 作者: 死貓他燒焦 時(shí)間: 2025-4-1 03:24
,Efficient Frequency-Domain Image Deraining with?Contrastive Regularization, capturing capabilities and efficiency. Simultaneously, the PGFN introduces residue channel prior in a gating manner to enhance local details and retain feature structure. Furthermore, we introduce a Frequency-domain Contrastive Regularization (FCR) during training. The FCR facilitates contrastive l作者: GRE 時(shí)間: 2025-4-1 09:18 作者: jarring 時(shí)間: 2025-4-1 11:03 作者: Eructation 時(shí)間: 2025-4-1 15:13 作者: Chandelier 時(shí)間: 2025-4-1 18:59
,Cascade-Zero123: One Image to?Highly Consistent 3D with?Self-prompted Nearby Views,eneration conditions. With amplified self-prompted condition images, our Cascade-Zero123 generates more consistent novel-view images than Zero-1-to-3. Experiment results demonstrate remarkable promotion, especially for various complex and challenging scenes, involving insects, humans, transparent ob作者: 發(fā)酵劑 時(shí)間: 2025-4-1 23:35
