Search Results for author:
Found 12 papers, 6 papers with code
Contrastive Sequential-Diffusion Learning: Non-linear and Multi-Scene Instructional Video Synthesis
Generated video scenes for action-centric sequence descriptions like recipe instructions and do-it-yourself projects include non-linear patterns, in which the next video may require to be visually consistent not on the immediate previous video but on earlier ones.
VideoPhy: Evaluating Physical Commonsense for Video Generation
Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render complex objects.
Generating Coherent Sequences of Visual Illustrations for Real-World Manual Tasks
In addition, to maintain the visual coherence of the image sequence, we introduce a copy mechanism to initialise reverse diffusion processes with a latent vector iteration from a previously generated image from a relevant step.
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation
For instance, we condition the visual features of the earlier and later scenes of the generated video with the representations of the first scene description (e. g., 'a red panda climbing a tree') and second scene description (e. g., 'the red panda sleeps on the top of the tree'), respectively.
Transferring Visual Attributes from Natural Language to Verified Image Generation
In this paper, we propose to strengthen the consistency property of T2I methods in the presence of natural complex language, which often breaks the limits of T2I methods by including non-visual information, and textual elements that require knowledge for accurate generation.
What You See is What You Read? Improving Text-Image Alignment Evaluation
Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks.
Ranked #11 on
Visual Reasoning
on Winoground
MaXM: Towards Multilingual Visual Question Answering
In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts.
MyStyle: A Personalized Generative Prior
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
Self-Distilled StyleGAN: Towards Generation from Internet Photos
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
A natural source for such attributes is the StyleSpace of StyleGAN, which is known to generate semantically meaningful dimensions in the image.
Semantic Pyramid for Image Generation
We demonstrate that our model results in a versatile and flexible framework that can be used in various classic and novel image generation tasks.
Temporal-Needle: A view and appearance invariant video descriptor
However, to find similar actions across videos, we consider only a small subset of the descriptors - the statistical significant descriptors.
