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Geometric 4D Stitching for Grounded 4D Generation

arXiv 2605.09984 ↗First posted 11 May 2026cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Recent 4D generation methods complete scene-level missing information using generative models and reconstruct the scene into radiance-based representations. However, these pipelines often present geometric inconsistencies in the generated content, and the radiance-based reconstruction requires expensive optimization. Furthermore, radiance-based representations often absorb these geometric inconsistencies into their view-dependent nature, failing to enforce the grounded geometric consistency. To address these issues, we propose Geometric 4D Stitching, an efficient framework that explicitly identifies missing geometric regions and complements them with geometrically grounded 4D stitches. As a result, our method constructs 4D scene representations in under 10 minutes on a single NVIDIA RTX 5090 GPU per one-step scene expansion, while improving geometric consistency. Moreover, we demonstrate that our explicit 4D stitching supports interative expansion of 4D mesh as well as 4D scene editing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 24 references that are in the map. First posted on arXiv 11 May 2026.

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19 of 24 references.

References · 19

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  3. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,261
  4. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
  5. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  6. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,157
  7. 2025SAM 3: Segment Anything with Conceptscited by 906
  8. 2025ReCamMaster: Camera-Controlled Generative Rendering from A Single Videocited by 287
  9. 2025AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Viewscited by 243
  10. 2022NeRF-Editing: Geometry Editing of Neural Radiance Fieldscited by 217
  11. 2024CAT4D: Create Anything in 4D with Multi-View Video Diffusion Modelscited by 161
  12. 2024SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistencycited by 144
7 more
  1. 2025TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Modelscited by 125
  2. 2022Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulationcited by 57
  3. 2025Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistencycited by 35
  4. 2026VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generationcited by 23
  5. 2025Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusioncited by 16
  6. 2026VGGT-World: Transforming VGGT into an Autoregressive Geometry World Modelcited by 13
  7. 2025Instant4D: 4D Gaussian Splatting in Minutescited by 10

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