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Found 5 papers, 2 papers with code
Lumiere: A Space-Time Diffusion Model for Video Generation
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #6 on
Text-to-Video Generation
on UCF-101
DreamBooth3D: Subject-Driven Text-to-3D Generation
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject.
Teaching CLIP to Count to Ten
Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count.
Imagic: Text-Based Real Image Editing with Diffusion Models
In this paper we demonstrate, for the very first time, the ability to apply complex (e. g., non-rigid) text-guided semantic edits to a single real image.
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
In this paper, we present a surprisingly simple yet highly effective method to mitigate this limitation: using pure noise images as additional training data.
Ranked #1 on
Long-tail Learning
on CelebA-5
