7 citations · 8 across the 4 of their papers we have counts for
12 papers · 1 filter
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
Rao Fu, Dingxi Zhang, Alex Jiang +4
Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale,…
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes
Xianghao Xu, Srinath Sridhar, Daniel Ritchie
We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optim…
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes
Rao Fu, Zehao Wen, Zichen Liu +1
Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text into well-structured and textured indoor scenes at a house-scale. By prompting Large Lang…
Strata-NeRF : Neural Radiance Fields for Stratified Scenes
Ankit Dhiman, Srinath R, Harsh Rangwani +4
Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settin…
DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields
Cheng-You Lu, Peisen Zhou, Angela Xing +8
Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes. However, their capabilities lag behind those offered by conventional…
HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Bipasha Sen, Gaurav Singh, Aditya Agarwal +3
Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects. However, learning generalizable…