Papers › Make Pixels Dance: High-Dynamic Video Generation

Make Pixels Dance: High-Dynamic Video Generation

18 Nov 2023CVPR 2024 1arXiv:2311.10982archive 2025-07-28

Yan Zeng, Guoqiang Wei, Jiani Zheng, Jiaxin Zou, Yang Wei, Yuchen Zhang, Hang Li

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

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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 MSR-VTT PixelDance CLIPSIM 0.3125 #5 of 18 Archive leaderboard report
Text-to-Video Generation MSR-VTT PixelDance FVD 381 #5 of 18 Archive leaderboard report
Text-to-Video Generation UCF-101 PixelDance (Zero-shot, 256x256) FVD16 242.82 #2 of 10 Archive leaderboard report
Video Generation UCF-101 PixelDance (256x256, text-conditional) FVD16 242.82 #17 of 48 Archive leaderboard report
Video Generation UCF-101 PixelDance (256x256, text-conditional) Inception Score 42.10 #17 of 48 Archive leaderboard report

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Methods

Diffusion

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