activity
20232026
most citedShow-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

15 citations · 30 across the 9 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

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…

cs.CV2024★ 3 cited

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Jinheng Xie, Weijia Mao, Zechen Bai +7

We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discret…

cs.CV2024

DragAnything: Motion Control for Anything using Entity Representation

Weijia Wu, Zhuang Li, Yuchao Gu +7

We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control m…

cs.CV2024★ 2 cited

Towards A Better Metric for Text-to-Video Generation

Jay Zhangjie Wu, Guian Fang, Haoning Wu +11

Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit imp…

cs.CV2024★ 2 cited

Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions

David Junhao Zhang, Dongxu Li, Hung Le +3

Most existing video diffusion models (VDMs) are limited to mere text conditions. Thereby, they are usually lacking in control over visual appearance and geometry structure of the g…