128 citations · 168 across the 12 of their papers we have counts for
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cs.CV2022★ 2 cited
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
cs.CV2022★ 26 cited
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen +2
We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt…
cs.CV2022★ 2 cited
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
Trevor Houchens, Cheng-You Lu, Shivam Duggal +2
In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for…