BulletGen: Improving 4D Reconstruction with Bullet-Time Generation

• URL: http://arxiv.org/abs/2506.18601v1
• Date: Mon, 23 Jun 2025 13:03:42 GMT
• Title: BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
• Abstract summary: We introduce BulletGen, an approach that takes advantage of generative models to correct errors and complete missing information in a dynamic scene representation.<n>Our method seamlessly blends generative content with both static and dynamic scene components, achieving state-of-the-art results on both novel-view synthesis, and 2D/3D tracking tasks.
• Score: 15.225127596594582
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transforming casually captured, monocular videos into fully immersive dynamic experiences is a highly ill-posed task, and comes with significant challenges, e.g., reconstructing unseen regions, and dealing with the ambiguity in monocular depth estimation. In this work we introduce BulletGen, an approach that takes advantage of generative models to correct errors and complete missing information in a Gaussian-based dynamic scene representation. This is done by aligning the output of a diffusion-based video generation model with the 4D reconstruction at a single frozen "bullet-time" step. The generated frames are then used to supervise the optimization of the 4D Gaussian model. Our method seamlessly blends generative content with both static and dynamic scene components, achieving state-of-the-art results on both novel-view synthesis, and 2D/3D tracking tasks.

Related papers

• World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video [62.16750462940772]
  We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.<n>Our approach conditions a video model on dense, pixel-aligned renderings that encode appearance, geometry, and 3D scene motion.<n>At test time, we distill the model's generations back into a single consistent, high-quality dynamic 3DGS, improving both novel-view synthesis and the underlying 3D motion.
  arXiv  Detail & Related papers  (2026-07-01T17:41:00Z)
• Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction [49.87425331212591]
  We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens.<n>C4G achieves strong novel-view synthesis performance using significantly fewer Gaussians and without requiring camera poses.
  arXiv  Detail & Related papers  (2026-05-29T17:57:41Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models [79.06910348413861]
  We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image.<n>Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion.
  arXiv  Detail & Related papers  (2025-11-01T11:16:25Z)
• 4D Driving Scene Generation With Stereo Forcing [62.47705572424127]
  Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-scene optimization.<n>We present PhiGenesis, a unified framework for 4D scene generation that extends video generation techniques with geometric and temporal consistency.
  arXiv  Detail & Related papers  (2025-09-24T15:37:17Z)
• Generative 4D Scene Gaussian Splatting with Object View-Synthesis Priors [22.797709893040906]
  GenMOJO is a novel approach that integrates rendering-based deformable 3D Gaussian optimization with generative priors for view synthesis.<n>It decomposes the scene into individual objects, optimizing a differentiable set of deformable Gaussians per object.<n>The resulting model generates 4D object reconstructions over space and time, and produces accurate 2D and 3D point tracks from monocular input.
  arXiv  Detail & Related papers  (2025-06-15T04:40:20Z)
• 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 [116.2042238179433]
  In this paper, we frame dynamic scenes as unconstrained 4D volume learning problems.<n>We represent a target dynamic scene using a collection of 4D Gaussian primitives with explicit geometry and appearance features.<n>This approach can capture relevant information in space and time by fitting the underlying photorealistic-temporal volume.<n> Notably, our 4DGS model is the first solution that supports real-time rendering of high-resolution, novel views for complex dynamic scenes.
  arXiv  Detail & Related papers  (2024-12-30T05:30:26Z)
• Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video [64.38566659338751]
  We propose the first 4D Gaussian Splatting framework to reconstruct a high-quality 4D model from blurry monocular video, named Deblur4DGS.<n>We introduce exposure regularization to avoid trivial solutions, as well as multi-frame and multi-resolution consistency ones to alleviate artifacts. Beyond novel-view, Deblur4DGS can be applied to improve blurry video from multiple perspectives, including deblurring, frame synthesis, and video stabilization.
  arXiv  Detail & Related papers  (2024-12-09T12:02:11Z)
• S4D: Streaming 4D Real-World Reconstruction with Gaussians and 3D Control Points [30.46796069720543]
  We introduce a novel approach for streaming 4D real-world reconstruction utilizing discrete 3D control points. This method physically models local rays and establishes a motion-decoupling coordinate system. By effectively merging traditional graphics with learnable pipelines, it provides a robust and efficient local 6-degrees-of-freedom (6 DoF) motion representation.
  arXiv  Detail & Related papers  (2024-08-23T12:51:49Z)
• EG4D: Explicit Generation of 4D Object without Score Distillation [105.63506584772331]
  DG4D is a novel framework that generates high-quality and consistent 4D assets without score distillation. Our framework outperforms the baselines in generation quality by a considerable margin.
  arXiv  Detail & Related papers  (2024-05-28T12:47:22Z)
• MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds [27.802537831023347]
  We introduce 4D Motion Scaffolds (MoSca), a modern 4D reconstruction system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casually in the wild.<n> Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks and its effectiveness on real videos.
  arXiv  Detail & Related papers  (2024-05-27T17:59:07Z)
• Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels [35.27805034331218]
  We present Vidu4D, a novel reconstruction model that excels in accurately reconstructing 4D representations from single generated videos. At the core of Vidu4D is our proposed Dynamic Gaussian Surfels (DGS) technique.
  arXiv  Detail & Related papers  (2024-05-27T04:43:44Z)
• SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer [57.506654943449796]
  We propose an efficient, sparse-controlled video-to-4D framework named SC4D that decouples motion and appearance. Our method surpasses existing methods in both quality and efficiency. We devise a novel application that seamlessly transfers motion onto a diverse array of 4D entities.
  arXiv  Detail & Related papers  (2024-04-04T18:05:18Z)
• DreamGaussian4D: Generative 4D Gaussian Splatting [56.49043443452339]
  We introduce DreamGaussian4D (DG4D), an efficient 4D generation framework that builds on Gaussian Splatting (GS) Our key insight is that combining explicit modeling of spatial transformations with static GS makes an efficient and powerful representation for 4D generation. Video generation methods have the potential to offer valuable spatial-temporal priors, enhancing the high-quality 4D generation.
  arXiv  Detail & Related papers  (2023-12-28T17:16:44Z)
• Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models [94.07744207257653]
  We focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects. We combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization.
  arXiv  Detail & Related papers  (2023-12-21T11:41:02Z)

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.