6 papers · 1 filter
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…
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…
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…