{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/make-pixels-dance-high-dynamic-video","title":"Make Pixels Dance: High-Dynamic Video Generation","arxiv_id":"2311.10982","date":"2023-11-18","proceeding":"CVPR 2024 1","authors":["Yan Zeng","Guoqiang Wei","Jiani Zheng","Jiaxin Zou","Yang Wei","Yuchen Zhang","Hang Li"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2311.10982v1","url_pdf":"https://arxiv.org/pdf/2311.10982v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"text-to-video-generation","task_name":"Text-to-Video Generation"},{"task_slug":"video-generation","task_name":"Video Generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-video-generation-on-msr-vtt","task":"Text-to-Video Generation","dataset":"MSR-VTT","model":"PixelDance","rank_in_archive_order":5,"of":18,"metrics":{"CLIPSIM":"0.3125","FVD":"381"},"uses_additional_data":false},{"leaderboard":"/sota/text-to-video-generation-on-ucf-101","task":"Text-to-Video Generation","dataset":"UCF-101","model":"PixelDance (Zero-shot, 256x256)","rank_in_archive_order":2,"of":10,"metrics":{"FVD16":"242.82"},"uses_additional_data":false},{"leaderboard":"/sota/video-generation-on-ucf-101","task":"Video Generation","dataset":"UCF-101","model":"PixelDance (256x256, text-conditional)","rank_in_archive_order":17,"of":48,"metrics":{"FVD16":"242.82","Inception Score":"42.10"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2311.10982","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}