title: Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal
    Consistency
summary: We present Free4D, a novel tuning-free framework for 4D scene generation from
  a single image. Existing methods either focus on object-level generation,
  making scene-level generation infeasible, or rely on large-scale multi-view
  video datasets for expensive training, with limited generalization ability due
  to the scarcity of 4D scene data. In contrast, our key insight is to distill
  pre-trained foundation models for consistent 4D scene representation, which
  offers promising advantages such as efficiency and generalizability. 1) To
  achieve this, we first animate the input image using image-to-video diffusion
  models followed by 4D geometric structure initialization. 2) To turn this
  coarse structure into spatial-temporal consistent multiview videos, we design
  an adaptive guidance mechanism with a point-guided denoising strategy for
  spatial consistency and a novel latent replacement strategy for temporal
  coherence. 3) To lift these generated observations into consistent 4D
  representation, we propose a modulation-based refinement to mitigate
  inconsistencies while fully leveraging the generated information. The resulting
  4D representation enables real-time, controllable rendering, marking a
  significant advancement in single-image-based 4D scene generation.
paper_authors._id: 67e7b4ba05d7355e476f4a10
paper_authors.name: Tianqi Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T08:12:50.975Z
paper_authors._id: 66d347eebb76fb26eedb256e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66d347eebb76fb26eedb256e/iCPF7GkmZu--XCsWzoucl.jpeg
paper_authors.fullname: tianqi liu
paper_authors.type: user
paper_authors.user: tqliu
paper_authors._id: 67e7b4ba05d7355e476f4a11
paper_authors.name: Zihao Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:33:22.318Z
paper_authors._id: 631b24f2f6bc4be4a64c4d43
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/631b24f2f6bc4be4a64c4d43/P9_tVF7SESmVxxGKVCgCk.jpeg
paper_authors.fullname: Zihao Huang
paper_authors.type: user
paper_authors.user: Inso
paper_authors._id: 67e7b4ba05d7355e476f4a12
paper_authors.name: Zhaoxi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:33:43.896Z
paper_authors._id: 62fc8cf7ee999004b5a8b982
paper_authors.avatarUrl: /avatars/6c5dda9e58747054a989f077a078f3dc.svg
paper_authors.fullname: Zhaoxi Chen
paper_authors.type: user
paper_authors.user: FrozenBurning
paper_authors._id: 67e7b4ba05d7355e476f4a13
paper_authors.name: Guangcong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:33:54.167Z
paper_authors._id: 62e893da40bd989bb71b8f89
paper_authors.avatarUrl: /avatars/34e755d1124303a498429a3c4d01367b.svg
paper_authors.fullname: Guangcong Wang
paper_authors.type: user
paper_authors.user: GuangcongWang
paper_authors._id: 67e7b4ba05d7355e476f4a14
paper_authors.name: Shoukang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:34:21.036Z
paper_authors._id: 6503be91a450492f84314af8
paper_authors.avatarUrl: /avatars/ef94efdad0bc8a423262d25a8cf77e41.svg
paper_authors.fullname: Shoukang Hu
paper_authors.type: user
paper_authors.user: skhu101
paper_authors._id: 67e7b4ba05d7355e476f4a15
paper_authors.name: Liao Shen
paper_authors._id: 67e7b4ba05d7355e476f4a16
paper_authors.name: Huiqiang Sun
paper_authors._id: 67e7b4ba05d7355e476f4a17
paper_authors.name: Zhiguo Cao
paper_authors._id: 67e7b4ba05d7355e476f4a18
paper_authors.name: Wei Li
paper_authors._id: 67e7b4ba05d7355e476f4a19
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-31T08:34:47.540Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal
    Consistency
paper_summary: We present Free4D, a novel tuning-free framework for 4D scene generation from
  a single image. Existing methods either focus on object-level generation,
  making scene-level generation infeasible, or rely on large-scale multi-view
  video datasets for expensive training, with limited generalization ability due
  to the scarcity of 4D scene data. In contrast, our key insight is to distill
  pre-trained foundation models for consistent 4D scene representation, which
  offers promising advantages such as efficiency and generalizability. 1) To
  achieve this, we first animate the input image using image-to-video diffusion
  models followed by 4D geometric structure initialization. 2) To turn this
  coarse structure into spatial-temporal consistent multiview videos, we design
  an adaptive guidance mechanism with a point-guided denoising strategy for
  spatial consistency and a novel latent replacement strategy for temporal
  coherence. 3) To lift these generated observations into consistent 4D
  representation, we propose a modulation-based refinement to mitigate
  inconsistencies while fully leveraging the generated information. The resulting
  4D representation enables real-time, controllable rendering, marking a
  significant advancement in single-image-based 4D scene generation.
paper_ai_summary: A novel tuning-free framework Free4D generates 4D scenes from a single image using image-to-video diffusion models, adaptive guidance, and a modulation-based refinement to ensure spatial and temporal consistency.
paper_ai_keywords: Free4D image-to-video diffusion models adaptive guidance point-guided denoising latent replacement modulation-based refinement 4D scene generation spatial consistency temporal coherence