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DreaMoving: A Human Video Generation Framework based on Diffusion Models

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arxiv 2312.05107 v2 pith:OTOLM7YW submitted 2023-12-08 cs.CV

Mengyang Feng , Jinlin Liu , Kai Yu , Yuan Yao , Zheng Hui , Xiefan Guo , Xianhui Lin , Haolan Xue
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Chen Shi Xiaowen Li Aojie Li Xiaoyang Kang Biwen Lei Miaomiao Cui Peiran Ren Xuansong Xie
This is my paper
classification cs.CV
keywords dreamovingvideoidentitydiffusionframeworkgenerategenerationhuman
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos. Specifically, given target identity and posture sequences, DreaMoving can generate a video of the target identity moving or dancing anywhere driven by the posture sequences. To this end, we propose a Video ControlNet for motion-controlling and a Content Guider for identity preserving. The proposed model is easy to use and can be adapted to most stylized diffusion models to generate diverse results. The project page is available at https://dreamoving.github.io/dreamoving

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-human Interactive Talking Dataset

    cs.CV 2025-08 conditional novelty 6.0 of 10

    The paper contributes a 12-hour multi-person conversational video dataset with pose and speaking annotations, plus a baseline model for generating full-body talking videos of two to four people.

  2. DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A diffusion model fuses per-person masks with pose keypoints to generate identity-preserving, two-person interactive videos from a single reference image, outperforming prior single-person-animation pipelines.

  3. Human Motion Video Generation: A Survey

    cs.CV 2025-09 conditional novelty 4.0 of 10

    A comprehensive survey with a five-phase pipeline model for human motion video generation, covering over 200 papers and adding a new benchmark comparison of nine pose-guided methods.

  4. Toward Rich Video Human-Motion2D Generation

    cs.CV 2025-06 reject novelty 4.0 of 10

    A new 150K-video 2D skeleton dataset with text captions and a diffusion model for single- and double-character motion generation, though the claimed FID-rewarded RL training is misrepresented.

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