Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

最高峰

, , : Semantic Grasp Generation via?Language Aligned Discretization,e the training of., we compile a large-scale, grasp-text-aligned dataset named., featuring over 300k detailed captions and 50k diverse grasps. Experimental findings demonstrate that.efficiently generates natural human grasps in alignment with linguistic intentions. Our code, models, and dataset are available publicly at: ..

精確

outer-ear

,VFusion3D: Learning Scalable 3D Generative Models from?Video Diffusion Models,enerative model. The proposed model, VFusion3D, trained on nearly 3M synthetic multi-view data, can generate a 3D asset from a single image in seconds and achieves superior performance when compared to current SOTA feed-forward 3D generative models, with users preferring our results over . of the time.

樸素

https://doi.org/10.1007/978-3-642-52015-0encoding for the drags and dataset randomization, the model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.

OASIS

配置

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. They deal with topics such as computer vision; machine learning; deep neural networks; r

憎惡

Die Eigenschaften der Staatsgewaltt can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition.

圖表證明

鄙視讀作

,Editable Image Elements for?Controllable Synthesis,t can faithfully reconstruct an input image. These elements can be intuitively edited by a user, and are decoded by a diffusion model into realistic images. We show the effectiveness of our representation on various image editing tasks, such as object resizing, rearrangement, dragging, de-occlusion, removal, variation, and image composition.

變化無(wú)常

,P2P-Bridge: Diffusion Bridges for?3D Point Cloud Denoising,RKitScenes, P2P-Bridge improves by a notable margin over existing methods. Although our method demonstrates promising results utilizing solely point coordinates, we demonstrate that incorporating additional features like RGB information and point-wise DINOV2 features further improves the results.Code and pretrained networks are available at ..