Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Hanocka, Rana"'
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed. Conveniently, the concepts can be defined as either text queries, e.g., "a dog" and "a turtle
Externí odkaz:
http://arxiv.org/abs/2408.14899
We cast multiview reconstruction from unknown pose as a generative modeling problem. From a collection of unannotated 2D images of a scene, our approach simultaneously learns both a network to predict camera pose from 2D image input, as well as the p
Externí odkaz:
http://arxiv.org/abs/2406.06972
We present iSeg, a new interactive technique for segmenting 3D shapes. Previous works have focused mainly on leveraging pre-trained 2D foundation models for 3D segmentation based on text. However, text may be insufficient for accurately describing fi
Externí odkaz:
http://arxiv.org/abs/2404.03219
Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, negle
Externí odkaz:
http://arxiv.org/abs/2404.16845
Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation. Creating ambigrams is a challenging task even for skilled artists, as it requires maintaining the meaning under two different viewpoints at the same
Externí odkaz:
http://arxiv.org/abs/2312.02967
In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes, producing texture maps which seamlessly integrate into standa
Externí odkaz:
http://arxiv.org/abs/2311.09571
Autor:
Babu, Sudarshan, Liu, Richard, Zhou, Avery, Maire, Michael, Shakhnarovich, Greg, Hanocka, Rana
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach are: (i) a dynamic hypernetwork, which learns a smooth mapping from te
Externí odkaz:
http://arxiv.org/abs/2310.17075
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently ex
Externí odkaz:
http://arxiv.org/abs/2307.15042
We present a technique for automatically producing a deformation of an input triangle mesh, guided solely by a text prompt. Our framework is capable of deformations that produce both large, low-frequency shape changes, and small high-frequency detail
Externí odkaz:
http://arxiv.org/abs/2304.13348
Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mimic the artistic style of specific artists after "fine-tuning" on sampl
Externí odkaz:
http://arxiv.org/abs/2302.04222