mlsanityData to 14 Oct 2026

A Compact Explicit 4D Representation for Dynamic Scenes

arXiv 2610.01229 ↗First posted 1 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

A compact dynamic-scene representation must retain both the surfaces seen over time and the appearance needed to render them from new viewpoints. We present Sparc4D, a feed-forward autoencoder that encodes a monocular video with known cameras into a sparse 4D scene state. Static features are shared across the clip, while spatially anchored temporal slots store time-varying features with a median 5.0× fewer values than per-frame storage and negligible change in the complete model's novel-view quality. A sparse decoder produces 2D Gaussian surfels, while stored source pixels preserve fine texture through geometric re-projection. The state includes one full source frame and dynamic-region pixels sampled every fourth frame, alongside learned features and sparse occupancy. For a 32-frame MultiCamVideo clip, it averages 0.67M 32-bit-equivalent values on random windows and 0.66M on the first-32 protocol. On first-32, Sparc4D reaches 21.97 dB, compared with 20.40 dB for MoVieS. On randomly placed windows, their PSNR scores are comparable. Without fine-tuning on real data, Sparc4D transfers to DyCheck and Neu3D, where stored texture improves LPIPS while slightly reducing PSNR. Frozen Sparc4D features also support a manipulation policy that reaches 81.3% success on ManiSkill3 StackCube from 1,000 demonstrations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 52 references that are in the map. First posted on arXiv 1 Oct 2026.

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42 of 52 references.

References · 42

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30 more
  1. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  2. 2023Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstructioncited by 759
  3. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 751
  4. 2023Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesiscited by 737
  5. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 703
  6. 2025Depth Anything 3: Recovering the Visual Space from Any Viewscited by 692
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  8. 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generationcited by 640
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  13. 2024DepthSplat: Connecting Gaussian Splatting and Depthcited by 286
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  24. 2025Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modelingcited by 89
  25. 2024STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenescited by 55
  26. 20254DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videoscited by 49
  27. 2025MoVieS: Motion-Aware 4D Dynamic View Synthesis in One Secondcited by 35
  28. 2026SceneTok: A Compressed, Diffusable Token Space for 3D Scenescited by 10
  29. 2026Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstructioncited by 2
  30. 2026VideoTok4D: A 4D-Aware Video Tokenizer for Compact World Representationcited by 1

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