PEEKABOO: Interactive Video Generation via Masked-Diffusion
Yash Jain Anshul Nasery Vibhav Vineet Harkirat Behl
Recently there has been a lot of progress in text-to-video generation, with
state-of-the-art models being capable of generating high quality, realistic
videos. However, these models lack the capability for users to interactively
control and generate videos, which can potentially unlock new areas of
application. As a first step towards this goal, we tackle the problem of
endowing diffusion-based video generation models with interactive
spatio-temporal control over their output. To this end, we take inspiration
from the recent advances in segmentation literature to propose a novel
spatio-temporal masked attention module - Peekaboo. This module is a
training-free, no-inference-overhead addition to off-the-shelf video generation
models which enables spatio-temporal control. We also propose an evaluation
benchmark for the interactive video generation task. Through extensive
qualitative and quantitative evaluation, we establish that Peekaboo enables
control video generation and even obtains a gain of upto 3.8x in mIoU over
baseline models.