No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos

• URL: http://arxiv.org/abs/2605.22190v1
• Date: Thu, 21 May 2026 08:57:21 GMT
• Title: No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos
• Abstract summary: NoPo4D is the first feed-forward system that addresses dynamic content, multi-view input, and unknown camera poses in a single pass.<n>On four multi-view dynamic benchmarks, NoPo4D consistently outperforms prior feed-forward baselines.
• Score: 49.02659692876764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent feed-forward 3D gaussian splatting methods have made dramatic progress on individual aspects of 3D scene reconstruction, but no existing method jointly addresses dynamic content, multi-view input, and unknown camera poses in a single feed-forward pass. Methods that handle dynamics either require accurate camera poses or accept only monocular input; pose-free multi-view methods address only static scenes; and per-scene optimization methods bridge some of these gaps but at minutes-to-hours cost per scene. We introduce NoPo4D, the first feed-forward system that addresses this empty quadrant. Building on a pretrained geometry backbone and recent 4D Gaussian frameworks, NoPo4D introduces a velocity decomposition that splits Gaussian motion into per-pixel image-plane shifts and depth changes, allowing direct supervision from pseudo ground-truth optical flow on the 2D component. This sidesteps both the differentiable rendering that couples prior posed methods to pose accuracy and the 3D motion ground truth that prior pose-free methods require. The system is rounded out by a bidirectional motion encoder for cross-view and cross-frame feature aggregation, and view-dependent opacity that mitigates cross-view and cross-timestep Gaussian misalignments. On four multi-view dynamic benchmarks, NoPo4D consistently outperforms prior feed-forward baselines, and with an optional post-optimization stage surpasses per-scene optimization methods, while running orders of magnitude faster.

Related papers

• Flow4R: Unifying 4D Reconstruction and Tracking with Scene Flow [61.297800738187355]
  Flow4R predicts a minimal per-pixel property set-3D point position, scene flow, pose weight, and confidence-from two-view inputs using a Vision Transformer.<n> trained jointly on static and dynamic datasets, Flow4R achieves state-of-the-art performance on 4D reconstruction and tracking tasks.
  arXiv  Detail & Related papers  (2026-02-15T06:58:08Z)
• Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding [54.859943475818234]
  We present Motion4D, a novel framework that integrates 2D priors from foundation models into a unified 4D Gaussian Splatting representation.<n>Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence.<n>Our method significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis.
  arXiv  Detail & Related papers  (2025-12-03T09:32:56Z)
• DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images [36.562825380568384]
  We introduce textbfDriving Gaussian Grounded Transformer (DGGT), a unified framework for pose-free dynamic scene reconstruction.<n>Our approach jointly predicts per-frame 3D Gaussian maps and camera parameters, disentangles dynamics with a lightweight dynamic head.<n>A diffusion-based rendering refinement further reduces motion/interpolation artifacts and improves novel-view quality under sparse inputs.
  arXiv  Detail & Related papers  (2025-12-02T18:29:18Z)
• 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)
• C4D: 4D Made from 3D through Dual Correspondences [77.04731692213663]
  We introduce C4D, a framework that leverages temporal correspondences to extend existing 3D reconstruction formulation to 4D.<n>C4D captures two types of correspondences: short-term optical flow and long-term point tracking.<n>We train a dynamic-aware point tracker that provides additional mobility information.
  arXiv  Detail & Related papers  (2025-10-16T17:59:06Z)
• In-2-4D: Inbetweening from Two Single-View Images to 4D Generation [63.68181731564576]
  We propose a new problem, Inbetween-2-4D, for generative 4D (i.e., 3D + motion) in interpolate two single-view images.<n>In contrast to video/4D generation from only text or a single image, our interpolative task can leverage more precise motion control to better constrain the generation.
  arXiv  Detail & Related papers  (2025-04-11T09:01:09Z)

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.