activity
20232026
most citedRealFill: Reference-Driven Generation for Authentic Image Completion

33 citations · 40 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

MultiGen: Level-Design for Editable Multiplayer Worlds in Diffusion Game Engines

Ryan Po, David Junhao Zhang, Amir Hertz +3

Video world models have shown immense promise for interactive simulation and entertainment, but current systems still struggle with two important aspects of interactivity: user con…

cs.CV2025

MotionV2V: Editing Motion in a Video

Ryan Burgert, Charles Herrmann, Forrester Cole +4

While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explo…

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★ 2 cited

Unbounded: A Generative Infinite Game of Character Life Simulation

Jialu Li, Yuanzhen Li, Neal Wadhwa +5

We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired b…

cs.CV2024

Magic Insert: Style-Aware Drag-and-Drop

Nataniel Ruiz, Yuanzhen Li, Neal Wadhwa +4

We present Magic Insert, a method for dragging-and-dropping subjects from a user-provided image into a target image of a different style in a physically plausible manner while matc…

cs.CV2023★ 5 cited

ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs

Viraj Shah, Nataniel Ruiz, Forrester Cole +4

Methods for finetuning generative models for concept-driven personalization generally achieve strong results for subject-driven or style-driven generation. Recently, low-rank adapt…

cs.CV2023★ 33 cited

RealFill: Reference-Driven Generation for Authentic Image Completion

Luming Tang, Nataniel Ruiz, Qinghao Chu +8

Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions. However, the c…