Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
Despite diffusion models having shown powerful abilities to generate
photorealistic images, generating videos that are realistic and diverse still
remains in its infancy. One of the key reasons is that current methods
intertwine spatial content and temporal dynamics together, leading to a notably
increased complexity of text-to-video generation (T2V). In this work, we
propose HiGen, a diffusion model-based method that improves performance by
decoupling the spatial and temporal factors of videos from two perspectives,
i.e., structure level and content level. At the structure level, we decompose
the T2V task into two steps, including spatial reasoning and temporal
reasoning, using a unified denoiser. Specifically, we generate spatially
coherent priors using text during spatial reasoning and then generate
temporally coherent motions from these priors during temporal reasoning. At the
content level, we extract two subtle cues from the content of the input video
that can express motion and appearance changes, respectively. These two cues
then guide the model's training for generating videos, enabling flexible
content variations and enhancing temporal stability. Through the decoupled
paradigm, HiGen can effectively reduce the complexity of this task and generate
realistic videos with semantics accuracy and motion stability. Extensive
experiments demonstrate the superior performance of HiGen over the
state-of-the-art T2V methods.
657291e1379284b904a705c7
Zhiwu Qing
657291e1379284b904a705c8
Shiwei Zhang
admin_assigned
2023-12-08T10:49:19.604Z
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Shiwei Zhang
user
StevenZhang
657291e1379284b904a705c9
Jiayu Wang
admin_assigned
2023-12-08T10:49:26.363Z
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Jiayu Wang
user
ArseneRean
657291e1379284b904a705ca
Xiang Wang
657291e1379284b904a705cb
Yujie Wei
claimed_verified
2024-04-11T07:44:49.668Z
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Yujie Wei
user
weilllllls
657291e1379284b904a705cc
Yingya Zhang
657291e1379284b904a705cd
Changxin Gao
657291e1379284b904a705ce
Nong Sang
Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
Despite diffusion models having shown powerful abilities to generate
photorealistic images, generating videos that are realistic and diverse still
remains in its infancy. One of the key reasons is that current methods
intertwine spatial content and temporal dynamics together, leading to a notably
increased complexity of text-to-video generation (T2V). In this work, we
propose HiGen, a diffusion model-based method that improves performance by
decoupling the spatial and temporal factors of videos from two perspectives,
i.e., structure level and content level. At the structure level, we decompose
the T2V task into two steps, including spatial reasoning and temporal
reasoning, using a unified denoiser. Specifically, we generate spatially
coherent priors using text during spatial reasoning and then generate
temporally coherent motions from these priors during temporal reasoning. At the
content level, we extract two subtle cues from the content of the input video
that can express motion and appearance changes, respectively. These two cues
then guide the model's training for generating videos, enabling flexible
content variations and enhancing temporal stability. Through the decoupled
paradigm, HiGen can effectively reduce the complexity of this task and generate
realistic videos with semantics accuracy and motion stability. Extensive
experiments demonstrate the superior performance of HiGen over the
state-of-the-art T2V methods.
HiGen is a diffusion model-based method that decouples spatial and temporal factors to improve text-to-video generation, achieving superior realism and motion stability.
diffusion models text-to-video generation T2V spatial reasoning temporal reasoning denoiser spatially coherent priors temporally coherent motions motion and appearance changes content variations temporal stability