2 citations · 2 across the 5 of their papers we have counts for
5 papers
What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models
Sharon S. Musa, Fereshteh Forghani, Harrish Thasarathan +3
Self-supervised video foundation models learn rich spatiotemporal representations, yet it remains unclear what visual concepts these representations encode, where they emerge acros…
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…
Can Generative Models Improve Self-Supervised Representation Learning?
Sana Ayromlou, Vahid Reza Khazaie, Fereshteh Forghani +1
The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the exi…
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…