Streaming 4D Visual Geometry Transformer

• URL: http://arxiv.org/abs/2507.11539v1
• Date: Tue, 15 Jul 2025 17:59:57 GMT
• Title: Streaming 4D Visual Geometry Transformer
• Authors: Dong Zhuo, Wenzhao Zheng, Jiahe Guo, Yuqi Wu, Jie Zhou, Jiwen Lu,
• Abstract summary: We propose a streaming 4D visual geometry transformer to process the input sequence in an online manner.<n>We use temporal causal attention and cache the historical keys and values as implicit memory to enable efficient streaming long-term 4D reconstruction.<n>Experiments on various 4D geometry perception benchmarks demonstrate that our model increases the inference speed in online scenarios.
• Score: 63.99937807085461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Perceiving and reconstructing 4D spatial-temporal geometry from videos is a fundamental yet challenging computer vision task. To facilitate interactive and real-time applications, we propose a streaming 4D visual geometry transformer that shares a similar philosophy with autoregressive large language models. We explore a simple and efficient design and employ a causal transformer architecture to process the input sequence in an online manner. We use temporal causal attention and cache the historical keys and values as implicit memory to enable efficient streaming long-term 4D reconstruction. This design can handle real-time 4D reconstruction by incrementally integrating historical information while maintaining high-quality spatial consistency. For efficient training, we propose to distill knowledge from the dense bidirectional visual geometry grounded transformer (VGGT) to our causal model. For inference, our model supports the migration of optimized efficient attention operator (e.g., FlashAttention) from the field of large language models. Extensive experiments on various 4D geometry perception benchmarks demonstrate that our model increases the inference speed in online scenarios while maintaining competitive performance, paving the way for scalable and interactive 4D vision systems. Code is available at: https://github.com/wzzheng/StreamVGGT.

Related papers

• 4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos [29.061337554486897]
  We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction.<n>Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic components.<n>Our model processes 64 consecutive posed frames in a rolling-window fashion, predicting consistent 4D Gaussians in the scene.
  arXiv  Detail & Related papers  (2025-06-09T17:59:59Z)
• Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency [49.875459658889355]
  Free4D is a tuning-free framework for 4D scene generation from a single image.<n>Our key insight is to distill pre-trained foundation models for consistent 4D scene representation.<n>The resulting 4D representation enables real-time, controllable rendering.
  arXiv  Detail & Related papers  (2025-03-26T17:59:44Z)
• 4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives [115.67081491747943]
  Dynamic 3D scene representation and novel view synthesis are crucial for enabling AR/VR and metaverse applications.<n>We reformulate the reconstruction of a time-varying 3D scene as approximating its underlying 4D volume.<n>We derive several compact variants that effectively reduce the memory footprint to address its storage bottleneck.
  arXiv  Detail & Related papers  (2024-12-30T05:30:26Z)
• Human4DiT: 360-degree Human Video Generation with 4D Diffusion Transformer [38.85054820740242]
  We present a novel approach for generating high-quality, coherent human videos from a single image. Our framework combines the strengths of diffusion transformers for capturing global correlations and CNNs for accurate condition injection. We demonstrate our method's ability to synthesize 360-degree realistic, coherent human motion videos.
  arXiv  Detail & Related papers  (2024-05-27T17:53:29Z)
• Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models [116.31344506738816]
  We present a novel framework, textbfDiffusion4D, for efficient and scalable 4D content generation. We develop a 4D-aware video diffusion model capable of synthesizing orbital views of dynamic 3D assets. Our method surpasses prior state-of-the-art techniques in terms of generation efficiency and 4D geometry consistency.
  arXiv  Detail & Related papers  (2024-05-26T17:47:34Z)
• 4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency [118.15258850780417]
  We present textbf4DGen, a novel framework for grounded 4D content creation.<n>Our pipeline facilitates controllable 4D generation, enabling users to specify the motion via monocular video or adopt image-to-video generations.<n>Compared to existing video-to-4D baselines, our approach yields superior results in faithfully reconstructing input signals.
  arXiv  Detail & Related papers  (2023-12-28T18:53:39Z)
• NSM4D: Neural Scene Model Based Online 4D Point Cloud Sequence Understanding [20.79861588128133]
  We introduce a generic online 4D perception paradigm called NSM4D. NSM4D serves as a plug-and-play strategy that can be adapted to existing 4D backbones. We demonstrate significant improvements on various online perception benchmarks in indoor and outdoor settings.
  arXiv  Detail & Related papers  (2023-10-12T13:42:49Z)
• Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction [43.60322886598972]
  This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. We present a novel pipeline to learn a temporal evolution of the 3D human shape through capturing continuous transformation functions among cross-frame occupancy fields.
  arXiv  Detail & Related papers  (2021-03-30T13:36:03Z)
• V4D:4D Convolutional Neural Networks for Video-level Representation Learning [58.548331848942865]
  Most 3D CNNs for video representation learning are clip-based, and thus do not consider video-temporal evolution of features. We propose Video-level 4D Conal Neural Networks, or V4D, to model long-range representation with 4D convolutions. V4D achieves excellent results, surpassing recent 3D CNNs by a large margin.
  arXiv  Detail & Related papers  (2020-02-18T09:27:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.