42 citations · 90 across the 6 of their papers we have counts for
5 papers · 1 filter
Learn Your Scales: Towards Scale-Consistent Generative Novel View Synthesis
Fereshteh Forghani, Jason J. Yu, Tristan Aumentado-Armstrong +2
Conventional depth-free multi-view datasets are captured using a moving monocular camera without metric calibration. The scales of camera positions in this monocular setting are am…
PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis
Jason J. Yu, Tristan Aumentado-Armstrong, Fereshteh Forghani +2
This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views. Here, we propose a s…
Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models
Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis +1
Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a larg…
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker
Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distrib…
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness
Jason J. Yu, Adam W. Harley, Konstantinos G. Derpanis
Recently, convolutional networks (convnets) have proven useful for predicting optical flow. Much of this success is predicated on the availability of large datasets that require ex…