Zobrazeno 1 - 10
of 726
pro vyhledávání: '"Kobbelt, Leif"'
Due to the fascinating generative performance of text-to-image diffusion models, growing text-to-3D generation works explore distilling the 2D generative priors into 3D, using the score distillation sampling (SDS) loss, to bypass the data scarcity pr
Externí odkaz:
http://arxiv.org/abs/2407.12684
Autor:
Elsner, Tim, Usinger, Paula, Czech, Victor, Kobsik, Gregor, He, Yanjiang, Lim, Isaak, Kobbelt, Leif
In quantised autoencoders, images are usually split into local patches, each encoded by one token. This representation is redundant in the sense that the same number of tokens is spend per region, regardless of the visual information content in that
Externí odkaz:
http://arxiv.org/abs/2407.11913
Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision. Neural radiance fields (NeRFs) can achieve realistic reconstruction and editing results but suffer from inefficiency in rendering. Gaus
Externí odkaz:
http://arxiv.org/abs/2404.09412
Inspired by the seminal result that a graph and an associated rotation system uniquely determine the topology of a closed manifold, we propose a combinatorial method for reconstruction of surfaces from points. Our method constructs a spanning tree an
Externí odkaz:
http://arxiv.org/abs/2402.01893
Symmetry detection, especially partial and extrinsic symmetry, is essential for various downstream tasks, like 3D geometry completion, segmentation, compression and structure-aware shape encoding or generation. In order to detect partial extrinsic sy
Externí odkaz:
http://arxiv.org/abs/2312.08230
Seam carving is an image editing method that enable content-aware resizing, including operations like removing objects. However, the seam-finding strategy based on dynamic programming or graph-cut limits its applications to broader visual data format
Externí odkaz:
http://arxiv.org/abs/2311.13297
Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological changes. Compared to classic geometry representations, however, neural rep
Externí odkaz:
http://arxiv.org/abs/2304.12951
Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images. Increasing sizes of a scene demands more complex functions, typically represented by neural networks, to capture all details. Training and inference th
Externí odkaz:
http://arxiv.org/abs/2303.16001
Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable
Externí odkaz:
http://arxiv.org/abs/2212.06556
The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enables intuitive shape editing in latent space by disentangling latent sub
Externí odkaz:
http://arxiv.org/abs/2111.12488