5 accepted papers
In the evolving landscape of video editing methodologies, a majority of deep learning techniques are often reliant on extensive datasets of observed input and ground truth sequence pairs for optimal performance. Such reliance often falters when acquiring data becomes challenging, especially in task…
Diffusion models have made significant strides in image generation mastering tasks such as unconditional image synthesis text-image translation and image-to-image conversions. However their capability falls short in the realm of video prediction mainly because they treat videos as a collection of in…
In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, and super-resolution, that does not require any external training data corpus. Our proposed approach directly learns the weights of neural modules by optimizing ov…
Generating future frames given a few context (or past) frames is a challenging task. It requires modeling the temporal coherence of videos as well as multi-modality in terms of diversity in the potential future states. Current variational approaches for video generation tend to marginalize over mult…
Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the…