1 citations · 1 across the 5 of their papers we have counts for
9 papers
SepGen: Multi-Stem Audio-Video Separation and Generation in a Single Model
Aviad Dahan, Rajaei Khatib, Yonatan Bitton +3
A 4D audio-visual scene comprises a video, the dynamic geometry it depicts, and the sound sources that populate it, each with its own position and trajectory. Rendering such a scen…
SemCam: Semantic Camera Motion Control for Video Generation
Janna Bruner, Omer Talmi, Ianir Ideses +3
Controlling the camera relative to a moving subject in an existing video is challenging: behaviors such as maintaining a frontal view require the camera to adapt to the subject's c…
Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
Yoad Tewel, Yuval Atzmon, Gal Chechik +1
Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits sca…
Compositional Video Generation via Inference-Time Guidance
Ariel Shaulov, Eitan Shaar, Amit Edenzon +2
Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attribut…
ID-LoRA: Identity-Driven Audio-Video Personalization with In-Context LoRA
Aviad Dahan, Moran Yanuka, Noa Kraicer +2
Existing video personalization methods preserve visual likeness but treat video and audio separately. Without access to the visual scene, audio models cannot synchronize sounds wit…
FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation
Ariel Shaulov, Itay Hazan, Lior Wolf +1
Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address thi…
A Meaningful Perturbation Metric for Evaluating Explainability Methods
Danielle Cohen, Hila Chefer, Lior Wolf
Deep neural networks (DNNs) have demonstrated remarkable success, yet their wide adoption is often hindered by their opaque decision-making. To address this, attribution methods ha…
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Hila Chefer, Uriel Singer, Amit Zohar +5
Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the convent…
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…