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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

DreaMoving: A Human Video Generation Framework based on Diffusion Models

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 T4 reserved
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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

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    CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.

  2. GenHSI: Controllable Generation of Human-Scene Interaction Videos

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    GenHSI is a training-free three-stage pipeline that turns a scene image, character image, and complex HSI prompt into long videos with plausible chained interactions by generating atomic actions, 3D keyframes via 2D i...

  3. ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity

    cs.CV 2026-04 unverdicted novelty 6.0

    ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.

  4. Human Motion Video Generation: A Survey

    cs.CV 2025-09 conditional novelty 4.0

    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.