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GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation

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arxiv 2403.12365 v2 pith:TKS6QK67 submitted 2024-03-19 cs.CV

Quankai Gao , Qiangeng Xu , Zhe Cao , Ben Mildenhall , Wenchao Ma , Le Chen , Danhang Tang , Ulrich Neumann This is my paper
classification cs.CV
keywords gaussiandynamicssplattingflowgenerationmethodnovelcontent
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Creating 4D fields of Gaussian Splatting from images or videos is a challenging task due to its under-constrained nature. While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored. In this paper, we introduce a novel concept, Gaussian flow, which connects the dynamics of 3D Gaussians and pixel velocities between consecutive frames. The Gaussian flow can be efficiently obtained by splatting Gaussian dynamics into the image space. This differentiable process enables direct dynamic supervision from optical flow. Our method significantly benefits 4D dynamic content generation and 4D novel view synthesis with Gaussian Splatting, especially for contents with rich motions that are hard to be handled by existing methods. The common color drifting issue that happens in 4D generation is also resolved with improved Guassian dynamics. Superior visual quality on extensive experiments demonstrates our method's effectiveness. Quantitative and qualitative evaluations show that our method achieves state-of-the-art results on both tasks of 4D generation and 4D novel view synthesis. Project page: https://zerg-overmind.github.io/GaussianFlow.github.io/

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Cited by 18 Pith papers

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

  1. ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment

    cs.CV 2026-08 conditional novelty 7.0 of 10

    ASTRA jointly estimates camera time offsets and dynamic Gaussian geometry by aligning projected 3D motion with observed 2D trajectory tracks, improving robustness to large asynchrony.

  2. MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    MoSA learns residual stress operators on an isotropic backbone using a physics-informed cascaded network and motion constraints to capture mild anisotropy and heterogeneity for improved real-to-sim dynamics.

  3. PaMoSplat: Part-Aware Motion-Guided Gaussian Splatting for Dynamic Scene Reconstruction

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    PaMoSplat reconstructs dynamic scenes by lifting 2D segmentations to coherent 3D Gaussian parts and estimating their motions via optical flow-guided differential evolution for higher quality rendering and faster training.

  4. GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow

    cs.RO 2026-04 unverdicted novelty 7.0 of 10

    GaussianFlow SLAM aligns projected Gaussian motion with optical flow to regularize monocular 3D Gaussian splatting SLAM, yielding better map quality and pose accuracy than prior methods.

  5. ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    ClipGStream enables scalable flicker-free reconstruction of long dynamic multi-view videos by performing stream optimization at the clip level with clip-independent spatio-temporal fields, residual anchor compensation...

  6. MoRight: Motion Control Done Right

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    MoRight disentangles object and camera motion via canonical-view specification and temporal cross-view attention, while decomposing motion into active user-driven and passive consequence components to learn and apply ...

  7. ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction

    cs.GR 2025-09 reject novelty 7.0 of 10

    ContraGS trains 3D Gaussian Splatting directly on codebook-compressed representations, cutting peak model memory ~3.5x with small quality loss.

  8. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

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

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    Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.

  10. AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling

    cs.CV 2026-07 accept novelty 6.0 of 10

    An asymmetric geometry-appearance architecture for generalizable 3DGS reallocates computation so smaller models match optimization-based NVS quality at ~800× speedup on 32-view 960P inputs while improving zero-shot results.

  11. Velox: Learning Representations of 4D Geometry and Appearance

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Velox compresses dynamic point clouds into latent tokens that support geometry via 4D surface modeling and appearance via 3D Gaussians, showing strong results on video-to-4D generation, tracking, and image-to-4D cloth...

  12. Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges

    cs.CV 2025-11 unverdicted novelty 6.0 of 10

    Splatography improves dynamic 3D reconstruction from sparse multi-view videos by splitting foreground and background Gaussian representations and applying tailored deformation learning for each.

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

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Hallo4D mitigates 3D/4D generation hallucinations via LMM-based detection, multi-model voting correction, and motion-aware optimization without retraining base generators.

  14. CP4D: Compositional Physics-aware 4D Scene Generation

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    CP4D generates physically consistent 4D scenes via compositional integration of pre-trained 3D models, hybrid simulator-diffusion motion synthesis, and automated scene composition.

  15. FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    Analysis of 4DGS reveals temporal partitioning from Gaussian durations and a photometric-spatiotemporal discrepancy, leading to FreeTimeGS++ with gated marginalization and neural velocity fields for superior stability...

  16. FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    FreeTimeGS++ improves dynamic scene reconstruction by identifying emergent temporal partitioning and photometric-motion decoupling in 4DGS, then applying targeted techniques for reduced run-to-run variance.

  17. FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    FreeTimeGS++ improves 4D Gaussian Splatting by using gated marginalization and neural velocity fields to achieve more stable dynamic scene representations with lower run-to-run variance.

  18. Advances in 4D Representation: Geometry, Motion, and Interaction

    cs.CV 2025-10 conditional novelty 4.0 of 10

    A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.

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