8 citations · 12 across the 11 of their papers we have counts for
5 papers · 1 filter
Orthogonal Adaptation for Modular Customization of Diffusion Models
Ryan Po, Guandao Yang, Kfir Aberman +1
Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications, enabling the generation of specific concepts across d…
Gaussian Shell Maps for Efficient 3D Human Generation
Rameen Abdal, Wang Yifan, Zifan Shi +6
Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks…
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…
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…
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…