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CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models

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arxiv 2411.18613 v2 pith:WMZR3532 submitted 2024-11-27 cs.CV

CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models

classification cs.CV
keywords videocat4dmulti-viewnoveldiffusiondynamicmodelmonocular
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transform a single monocular video into a multi-view video, enabling robust 4D reconstruction via optimization of a deformable 3D Gaussian representation. We demonstrate competitive performance on novel view synthesis and dynamic scene reconstruction benchmarks, and highlight the creative capabilities for 4D scene generation from real or generated videos. See our project page for results and interactive demos: https://cat-4d.github.io/.

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Cited by 12 Pith papers

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

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  3. PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

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    PerpetualWonder introduces a closed-loop generative simulator with a unified physical-visual representation for long-horizon action-conditioned 4D scene generation from one image.

  4. A Scene is Worth a Thousand Features: Feed-Forward Camera Localization from a Collection of Image Features

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  5. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

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  7. Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis

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  11. BulletGen: Improving 4D Reconstruction with Bullet-Time Generation

    cs.GR 2025-06 unverdicted novelty 6.0

    BulletGen enhances 4D dynamic scene reconstruction from monocular videos by supervising Gaussian optimization with diffusion-generated frames aligned at a bullet-time step, achieving SOTA on novel-view synthesis and tracking.

  12. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

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