Papers › VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

30 Oct 2023arXiv:2310.19512archive 2025-07-28

Haoxin Chen, Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, Chao Weng, Ying Shan

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 ×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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Code

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ailab-cvc/videocrafter officialmentioned in papermentioned on GitHubpytorchNOASSERTION report
invictus717/interactivevideo mentioned on GitHubpytorchApache-2.0 report
videocrafter/videocrafter mentioned on GitHubpytorch report

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denormalize invictus717/interactivevideo/models/streamdiffusion/image_utils.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · 5dd5ccc011afb9c3 · report
is_installed invictus717/interactivevideo/models/streamdiffusion/pip_utils.py community (archive-listed) ran Apache-2.0 (permissive) · 35d471d9e81ee42a · report
numpy_to_pil invictus717/interactivevideo/models/streamdiffusion/image_utils.py community (archive-listed) ran Apache-2.0 (permissive) · b188597984f52631 · report
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version invictus717/interactivevideo/models/streamdiffusion/pip_utils.py community (archive-listed) ran Apache-2.0 (permissive) · 635ea667a5c67443 · report
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Tasks

Text-to-Video GenerationVideo Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text-to-Video Generation EvalCrafter Text-to-Video (ECTV) Dataset VideoCrafter1 Motion Quality 60.85 #3 of 5 Archive leaderboard report
Text-to-Video Generation EvalCrafter Text-to-Video (ECTV) Dataset VideoCrafter1 Temporal Consistency 55.89 #3 of 5 Archive leaderboard report
Text-to-Video Generation EvalCrafter Text-to-Video (ECTV) Dataset VideoCrafter1 Text-to-Video Alignment 61.95 #3 of 5 Archive leaderboard report
Text-to-Video Generation EvalCrafter Text-to-Video (ECTV) Dataset VideoCrafter1 Total Score 232 #3 of 5 Archive leaderboard report
Text-to-Video Generation EvalCrafter Text-to-Video (ECTV) Dataset VideoCrafter1 Visual Quality 53.08 #3 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CLIPDiffusion

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