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Direct Motion Models for Assessing Generated Videos
Kelsey Allen, Carl Doersch, Guangyao Zhou +9
A current limitation of video generative video models is that they generate plausible looking frames, but poor motion -- an issue that is not well captured by FVD and other popular…
Spectral Image Tokenizer
Carlos Esteves, Mohammed Suhail, Ameesh Makadia
Image tokenizers map images to sequences of discrete tokens, and are a crucial component of autoregressive transformer-based image generation. The tokens are typically associated w…
Factorized Video Autoencoders for Efficient Generative Modelling
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Latent variable generative models have emerged as powerful tools for generative tasks including image and video synthesis. These models are enabled by pretrained autoencoders that…
Generalizable Patch-Based Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably. The…
Light Field Neural Rendering
Mohammed Suhail, Carlos Esteves, Leonid Sigal +1
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view…
Segmentation-grounded Scene Graph Generation
Siddhesh Khandelwal, Mohammed Suhail, Leonid Sigal
Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an…
Energy-Based Learning for Scene Graph Generation
Mohammed Suhail, Abhay Mittal, Behjat Siddiquie +4
Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores…