title: Enabling Versatile Controls for Video Diffusion Models
summary: Despite substantial progress in text-to-video generation, achieving precise
  and flexible control over fine-grained spatiotemporal attributes remains a
  significant unresolved challenge in video generation research. To address these
  limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework
  designed to enable fine-grained control over pre-trained video diffusion models
  in a unified manner. VCtrl integrates diverse user-specified control
  signals-such as Canny edges, segmentation masks, and human keypoints-into
  pretrained video diffusion models via a generalizable conditional module
  capable of uniformly encoding multiple types of auxiliary signals without
  modifying the underlying generator. Additionally, we design a unified control
  signal encoding pipeline and a sparse residual connection mechanism to
  efficiently incorporate control representations. Comprehensive experiments and
  human evaluations demonstrate that VCtrl effectively enhances controllability
  and generation quality. The source code and pre-trained models are publicly
  available and implemented using the PaddlePaddle framework at
  http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.
paper_authors._id: 67e0c303ff27a08e3896134a
paper_authors.name: Xu Zhang
paper_authors._id: 67e0c303ff27a08e3896134b
paper_authors.name: Hao Zhou
paper_authors._id: 67e0c303ff27a08e3896134c
paper_authors.name: Haoming Qin
paper_authors._id: 67e0c303ff27a08e3896134d
paper_authors.name: Xiaobin Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:47:23.819Z
paper_authors._id: 64a42dc94c10e3675498ad92
paper_authors.avatarUrl: /avatars/1f3e027d27af8444a437399d9ee6dd8f.svg
paper_authors.fullname: xiaobin Lu
paper_authors.type: user
paper_authors.user: xilu5047
paper_authors._id: 67e0c303ff27a08e3896134e
paper_authors.name: Jiaxing Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:46:19.494Z
paper_authors._id: 67c4610fa8ec9d71bf3b2a48
paper_authors.avatarUrl: /avatars/88eb9641e1dc7a237498c5257a0232aa.svg
paper_authors.fullname: yanjiaxing
paper_authors.type: user
paper_authors.user: YanJiaXing
paper_authors._id: 67e0c303ff27a08e3896134f
paper_authors.name: Guanzhong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:47:37.219Z
paper_authors._id: 677f5eca19fb7f8c452e0876
paper_authors.avatarUrl: /avatars/f053709ece377164975e266e52ab23af.svg
paper_authors.fullname: wang guanzhong
paper_authors.type: user
paper_authors.user: jerrywgz
paper_authors._id: 67e0c303ff27a08e38961350
paper_authors.name: Zeyu Chen
paper_authors._id: 67e0c303ff27a08e38961351
paper_authors.name: Yi Liu
paper_title: Enabling Versatile Controls for Video Diffusion Models
paper_summary: Despite substantial progress in text-to-video generation, achieving precise
  and flexible control over fine-grained spatiotemporal attributes remains a
  significant unresolved challenge in video generation research. To address these
  limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework
  designed to enable fine-grained control over pre-trained video diffusion models
  in a unified manner. VCtrl integrates diverse user-specified control
  signals-such as Canny edges, segmentation masks, and human keypoints-into
  pretrained video diffusion models via a generalizable conditional module
  capable of uniformly encoding multiple types of auxiliary signals without
  modifying the underlying generator. Additionally, we design a unified control
  signal encoding pipeline and a sparse residual connection mechanism to
  efficiently incorporate control representations. Comprehensive experiments and
  human evaluations demonstrate that VCtrl effectively enhances controllability
  and generation quality. The source code and pre-trained models are publicly
  available and implemented using the PaddlePaddle framework at
  http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.
paper_ai_summary: VCtrl enhances control over fine-grained spatiotemporal attributes in text-to-video generation by integrating various control signals into pre-trained video diffusion models without modifying the generator.
paper_ai_keywords: text-to-video generation fine-grained spatiotemporal attributes VCtrl PP-VCtrl Canny edges segmentation masks human keypoints conditional module auxiliary signals unified control signal encoding pipeline sparse residual connection mechanism camera diffusion models generation quality