35 citations · 66 across the 9 of their papers we have counts for
9 papers
TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
Daniel Garibi, Shahar Yadin, Roni Paiss +6
We present TokenVerse -- a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model. Our framework can disentangle complex visual elements a…
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
David Junhao Zhang, Roni Paiss, Shiran Zada +7
Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided…
VidPanos: Generative Panoramic Videos from Casual Panning Videos
Jingwei Ma, Erika Lu, Roni Paiss +6
Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view. Stitching frames of a panning video into a panoramic photog…
Still-Moving: Customized Video Generation without Customized Video Data
Hila Chefer, Shiran Zada, Roni Paiss +7
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expan…
Lumiere: A Space-Time Diffusion Model for Video Generation
Omer Bar-Tal, Hila Chefer, Omer Tov +14
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 synthes…
DreamBooth3D: Subject-Driven Text-to-3D Generation
Amit Raj, Srinivas Kaza, Ben Poole +9
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in…
Teaching CLIP to Count to Ten
Roni Paiss, Ariel Ephrat, Omer Tov +4
Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classificati…
Imagic: Text-Based Real Image Editing with Diffusion Models
Bahjat Kawar, Shiran Zada, Oran Lang +5
Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, sty…
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Shiran Zada, Itay Benou, Michal Irani
Despite remarkable progress on visual recognition tasks, deep neural-nets still struggle to generalize well when training data is scarce or highly imbalanced, rendering them extrem…