Search Results for author:
Found 8 papers, 7 papers with code
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models
In RDMs, a set of nearest neighbors is retrieved from an external database during training for each training instance, and the diffusion model is conditioned on these informative samples.
Semi-Parametric Neural Image Synthesis
Much of this success is due to the scalability of these architectures and hence caused by a dramatic increase in model complexity and in the computational resources invested in training these models.
High-Resolution Image Synthesis with Latent Diffusion Models
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
Ranked #4 on
Image Generation
on ImageNet 512x512
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis
Thus, in contrast to pure autoregressive models, it can solve free-form image inpainting and, in the case of conditional models, local, text-guided image modification without requiring mask-specific training.
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
There will be distinctive movement, despite evident variations caused by the stochastic nature of our world.
Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time.
Stochastic Image-to-Video Synthesis using cINNs
Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.
Behavior-Driven Synthesis of Human Dynamics
Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence.
