Search Results for author:
Found 8 papers, 2 papers with code
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video.
VidPanos: Generative Panoramic Videos from Casual Panning Videos
Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view.
Still-Moving: Customized Video Generation without Customized Video Data
We assume access to a customized version of the T2I model, trained only on still image data (e. g., using DreamBooth or StyleDrop).
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
