8 citations · 12 across the 11 of their papers we have counts for
13 papers
MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines
Ryan Po, David Junhao Zhang, Amir Hertz +3
Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user con…
BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models
Ryan Po, Eric Ryan Chan, Changan Chen +1
Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and infere…
Video World Models with Long-term Spatial Memory
Tong Wu, Shuai Yang, Ryan Po +4
Emerging world models autoregressively generate video frames in response to actions, such as camera movements and text prompts, among other control signals. Due to limited temporal…
Long-Context State-Space Video World Models
Ryan Po, Yotam Nitzan, Richard Zhang +5
Video diffusion models have recently shown promise for world modeling through autoregressive frame prediction conditioned on actions. However, they struggle to maintain long-term m…
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models
Tong Wu, Yinghao Xu, Ryan Po +6
Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can…
Flying with Photons: Rendering Novel Views of Propagating Light
Anagh Malik, Noah Juravsky, Ryan Po +3
We present an imaging and neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Our approach relies…
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…
3D Neural Field Generation using Triplane Diffusion
J. Ryan Shue, Eric Ryan Chan, Ryan Po +3
Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural…
Adaptive Gating for Single-Photon 3D Imaging
Ryan Po, Adithya Pediredla, Ioannis Gkioulekas
Single-photon avalanche diodes (SPADs) are growing in popularity for depth sensing tasks. However, SPADs still struggle in the presence of high ambient light due to the effects of…