VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Despite tremendous progress in the field of text-to-video (T2V) synthesis,
open-sourced T2V diffusion models struggle to generate longer videos with
dynamically varying and evolving content. They tend to synthesize quasi-static
videos, ignoring the necessary visual change-over-time implied in the text
prompt. At the same time, scaling these models to enable longer, more dynamic
video synthesis often remains computationally intractable. To address this
challenge, we introduce the concept of Generative Temporal Nursing (GTN), where
we aim to alter the generative process on the fly during inference to improve
control over the temporal dynamics and enable generation of longer videos. We
propose a method for GTN, dubbed VSTAR, which consists of two key ingredients:
1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis
based on the original single prompt leveraging LLMs, which gives accurate
textual guidance to different visual states of longer videos, and 2) Temporal
Attention Regularization (TAR) - a regularization technique to refine the
temporal attention units of the pre-trained T2V diffusion models, which enables
control over the video dynamics. We experimentally showcase the superiority of
the proposed approach in generating longer, visually appealing videos over
existing open-sourced T2V models. We additionally analyze the temporal
attention maps realized with and without VSTAR, demonstrating the importance of
applying our method to mitigate neglect of the desired visual change over time.
65fbb4febeb503364b8210fd
Yumeng Li
claimed_verified
2024-03-21T10:21:19.202Z
62a8e65cbad2c679dc0b2866
/avatars/08f06947880f74145005f0da1a82dfed.svg
Yumeng Li
user
Yumeng
65fbb4febeb503364b8210fe
William Beluch
65fbb4febeb503364b8210ff
Margret Keuper
65fbb4febeb503364b821100
Dan Zhang
65fbb4febeb503364b821101
Anna Khoreva
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Despite tremendous progress in the field of text-to-video (T2V) synthesis,
open-sourced T2V diffusion models struggle to generate longer videos with
dynamically varying and evolving content. They tend to synthesize quasi-static
videos, ignoring the necessary visual change-over-time implied in the text
prompt. At the same time, scaling these models to enable longer, more dynamic
video synthesis often remains computationally intractable. To address this
challenge, we introduce the concept of Generative Temporal Nursing (GTN), where
we aim to alter the generative process on the fly during inference to improve
control over the temporal dynamics and enable generation of longer videos. We
propose a method for GTN, dubbed VSTAR, which consists of two key ingredients:
1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis
based on the original single prompt leveraging LLMs, which gives accurate
textual guidance to different visual states of longer videos, and 2) Temporal
Attention Regularization (TAR) - a regularization technique to refine the
temporal attention units of the pre-trained T2V diffusion models, which enables
control over the video dynamics. We experimentally showcase the superiority of
the proposed approach in generating longer, visually appealing videos over
existing open-sourced T2V models. We additionally analyze the temporal
attention maps realized with and without VSTAR, demonstrating the importance of
applying our method to mitigate neglect of the desired visual change over time.
Generative Temporal Nursing (GTN) with VSTAR enhances temporal dynamics in text-to-video synthesis by generating video synopses and regularizing temporal attention.
Generative Temporal Nursing GTN VSTAR Video Synopsis Prompting VSP Temporal Attention Regularization TAR T2V diffusion models temporal dynamics visual change over time