Streaming4D: Accelerate 4D World Models via Block-wise Video Generation and Incremental Reconstruction
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
Current 4D generation paradigms are often bottlenecked by a sequential decoupling design: video is generated first, followed by 3D reconstruction, leading to high interaction latency. This limits applications in interactive real-time scenarios. To this end, we propose Streaming4D, a tightly coupled synchronous pipeline that integrates block-wise autoregressive video generation with incremental 3D reconstruction. Unlike traditional frame-by-frame emission and delayed geometry recovery, Streaming4D generates temporal video blocks and immediately triggers reconstruction for each completed block, enabling parallel execution between synthesis and geometric updates. This approach allows the world representation to evolve online with the video stream, reducing feedback latency while preserving geometric fidelity. We instantiate Streaming4D using a Self-Forcing-style autoregressive generator and an incremental reconstruction backend. Experiments show consistent runtime improvements across resolutions on a single RTX 4090 (1.24× speedup), while maintaining high-quality 4D geometry and multi-view consistency.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 25 of its 51 references that are in the map. First posted on arXiv 1 Sep 2026.
Builds on
25 of 51 references.
Closest ancestors · 2
Same direction, newest first
- 2025TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model31 Dec
- 2025Rolling Forcing: Autoregressive Long Video Diffusion in Real Time29 Sep
Other references · 23
- 2025Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusioncited by 625
- 2022Phenaki: Variable Length Video Generation From Open Domain Textual Descriptioncited by 484
- 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 442
- 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Modelscited by 433
- 2023DreamGaussian4D: Generative 4D Gaussian Splattingcited by 194
- 2025FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Drivingcited by 166
- 2025Streaming 4D Visual Geometry Transformercited by 161
- 20234DGen: Grounded 4D Content Generation with Spatial-temporal Consistencycited by 117
- 2025JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigationcited by 89
- 2025Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memorycited by 87
- 2025STream3R: Scalable Sequential 3D Reconstruction with Causal Transformercited by 82
- 2024Autoregressive Models in Vision: A Surveycited by 39
11 more
- 20244D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenescited by 37
- 20254DNeX: Feed-Forward 4D Generative Modeling Made Easycited by 34
- 2025Playing with Transformer at 30+ FPS via Next-Frame Diffusioncited by 20
- 2025Test3R: Learning to Reconstruct 3D at Test Timecited by 20
- 2025AR4D: Autoregressive 4D Generation from Monocular Videoscited by 16
- 20254Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generationcited by 14
- 2026Advances in Global Solvers for 3D Visioncited by 13
- 2025STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flowscited by 9
- 2025Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Modelscited by 7
- 2026AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placementcited by 2
- 2025Dream4D: Lifting Camera-Controlled I2V towards Spatiotemporally Consistent 4D Generationcited by 1
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- 2026Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
- 2025WorldReel: 4D Video Generation with Consistent Geometry and Motion Modeling
- 20264LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling
- 2026Beyond Pixels: From Video Priors to 4D Worlds
- 20264DStreamCtrl: Interactive Video Generation with Online 4D Control
- 2026Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution
- 2026Feed-forward Motion In-betweening for Any 4D
- 2026VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control