VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Video generation has increasingly gained interest in both academia and
industry. Although commercial tools can generate plausible videos, there is a
limited number of open-source models available for researchers and engineers.
In this work, we introduce two diffusion models for high-quality video
generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V
models synthesize a video based on a given text input, while I2V models
incorporate an additional image input. Our proposed T2V model can generate
realistic and cinematic-quality videos with a resolution of 1024 times 576,
outperforming other open-source T2V models in terms of quality. The I2V model
is designed to produce videos that strictly adhere to the content of the
provided reference image, preserving its content, structure, and style. This
model is the first open-source I2V foundation model capable of transforming a
given image into a video clip while maintaining content preservation
constraints. We believe that these open-source video generation models will
contribute significantly to the technological advancements within the
community.
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Haoxin Chen
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2024-01-19T09:23:37.348Z
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Haoxin Chen
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Menghan Xia
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2023-10-31T08:49:50.538Z
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Richard
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menghanxia
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Yingqing He
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2023-10-31T08:50:09.144Z
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He
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Yingqing
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Yong Zhang
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Xiaodong Cun
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2023-10-31T08:51:06.229Z
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Xiaodong Cun
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vinthony
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Shaoshu Yang
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Jinbo Xing
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2023-10-31T08:51:38.054Z
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Jinbo Xing
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Doubiiu
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Yaofang Liu
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2024-01-31T08:59:05.540Z
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Raphael Liu
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RaphaelLiu
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Qifeng Chen
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2023-10-31T08:51:58.343Z
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Qifeng Chen
user
cqf
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Xintao Wang
admin_assigned
2023-10-31T08:53:20.343Z
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Xintao Wang
user
Xintao
654094611c4b203c01300de0
Chao Weng
654094611c4b203c01300de1
Ying Shan
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Video generation has increasingly gained interest in both academia and
industry. Although commercial tools can generate plausible videos, there is a
limited number of open-source models available for researchers and engineers.
In this work, we introduce two diffusion models for high-quality video
generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V
models synthesize a video based on a given text input, while I2V models
incorporate an additional image input. Our proposed T2V model can generate
realistic and cinematic-quality videos with a resolution of 1024 times 576,
outperforming other open-source T2V models in terms of quality. The I2V model
is designed to produce videos that strictly adhere to the content of the
provided reference image, preserving its content, structure, and style. This
model is the first open-source I2V foundation model capable of transforming a
given image into a video clip while maintaining content preservation
constraints. We believe that these open-source video generation models will
contribute significantly to the technological advancements within the
community.
Two diffusion models, T2V and I2V, generate high-quality videos from text and image inputs respectively, with T2V producing realistic cinematic videos and I2V preserving the content of the reference image.
diffusion models text-to-video image-to-video realistic videos cinematic quality content preservation