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pro vyhledávání: '"Trevithick, Alex"'
We introduce RealmDreamer, a technique for generation of general forward-facing 3D scenes from text descriptions. Our technique optimizes a 3D Gaussian Splatting representation to match complex text prompts. We initialize these splats by utilizing th
Externí odkaz:
http://arxiv.org/abs/2404.07199
Autor:
Trevithick, Alex, Chan, Matthew, Takikawa, Towaki, Iqbal, Umar, De Mello, Shalini, Chandraker, Manmohan, Ramamoorthi, Ravi, Nagano, Koki
3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet, the significant memory and
Externí odkaz:
http://arxiv.org/abs/2401.02411
Autor:
Lin, Kai-En, Trevithick, Alex, Cheng, Keli, Sarkis, Michel, Ghafoorian, Mohsen, Bi, Ning, Reitmayr, Gerhard, Ramamoorthi, Ravi
Portrait synthesis creates realistic digital avatars which enable users to interact with others in a compelling way. Recent advances in StyleGAN and its extensions have shown promising results in synthesizing photorealistic and accurate reconstructio
Externí odkaz:
http://arxiv.org/abs/2306.17123
Autor:
Trevithick, Alex, Chan, Matthew, Stengel, Michael, Chan, Eric R., Liu, Chao, Yu, Zhiding, Khamis, Sameh, Chandraker, Manmohan, Ramamoorthi, Ravi, Nagano, Koki
We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a
Externí odkaz:
http://arxiv.org/abs/2305.02310
Autor:
Gu, Jiatao, Trevithick, Alex, Lin, Kai-En, Susskind, Josh, Theobalt, Christian, Liu, Lingjie, Ramamoorthi, Ravi
Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. Existing approaches condition neural radiance fields (NeRF) on loca
Externí odkaz:
http://arxiv.org/abs/2302.10109
Autor:
Trevithick, Alex, Yang, Bo
We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a general radiance field, which takes a set of 2D images with camera poses and
Externí odkaz:
http://arxiv.org/abs/2010.04595