8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.AI2023★ 8 cited
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
cs.CV2023
Instant Continual Learning of Neural Radiance Fields
Ryan Po, Zhengyang Dong, Alexander W. Bergman +1
Neural radiance fields (NeRFs) have emerged as an effective method for novel-view synthesis and 3D scene reconstruction. However, conventional training methods require access to al…
cs.CV2023★ 1 cited
Compositional 3D Scene Generation using Locally Conditioned Diffusion
Ryan Po, Gordon Wetzstein
Designing complex 3D scenes has been a tedious, manual process requiring domain expertise. Emerging text-to-3D generative models show great promise for making this task more intuit…