WildPose: A Unified Framework for Robust Pose Estimation in the Wild

• URL: http://arxiv.org/abs/2605.12774v1
• Date: Tue, 12 May 2026 21:39:44 GMT
• Title: WildPose: A Unified Framework for Robust Pose Estimation in the Wild
• Abstract summary: We present WildPose, a unified monocular pose framework that is robust in dynamic environments.<n>Our key insight is to connect two powerful paradigms in modern 3D vision: the rich perceptual estimation of feedforward models and the end-to-end optimization of differentiable bundle adjustment (BA)
• Score: 13.59516321308291
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Estimating camera pose in dynamic environments is a critical challenge, as most visual SLAM and SfM methods assume static scenes. While recent dynamic-aware methods exist, they are often not unified: semantic-based approaches are brittle, per-sequence optimization methods fail on short sequences, and other learned models may degrade on static-only scenes. We present WildPose, a unified monocular pose estimation framework that is robust in dynamic environments while maintaining state-of-the-art performance on static and low-ego-motion datasets. Our key insight is to connect two powerful paradigms in modern 3D vision: the rich perceptual frontend of feedforward models and the end-to-end optimization of differentiable bundle adjustment (BA). We achieve this with a 3D-aware update operator built on a frozen, pre-trained MASt3R feature backbone, together with a high-capacity motion mask detector that uses multi-level 3D-aware features from the same backbone. Extensive experiments show WildPose consistently outperforms prior methods across dynamic (Wild-SLAM, Bonn), static (TUM, 7-Scenes), and low-ego-motion (Sintel) benchmarks.

Related papers

• JOintGS: Joint Optimization of Cameras, Bodies and 3D Gaussians for In-the-Wild Monocular Reconstruction [18.636227266388218]
  We present JOintGS, a unified framework that jointly optimize camera extrinsics, human poses, and 3D Gaussian representations.<n>Experiments on NeuMan and EMDB datasets demonstrate that JOintGS achieves superior reconstruction quality.
  arXiv  Detail & Related papers  (2026-02-04T08:33:51Z)
• EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis [43.898895514609286]
  EvolSplat4D is a feed-forward framework that moves beyond existing per-pixel paradigms by unifying volume-based and pixel-based Gaussian prediction.<n>We show that EvolSplat4D reconstructs both static and dynamic environments with superior accuracy and consistency, outperforming both per-scene optimization and state-of-the-art feed-forward baselines.
  arXiv  Detail & Related papers  (2026-01-22T13:39:29Z)
• DePT3R: Joint Dense Point Tracking and 3D Reconstruction of Dynamic Scenes in a Single Forward Pass [2.0487171253259104]
  DePT3R is a novel framework that simultaneously performs dense point tracking and 3D reconstruction of dynamic scenes from multiple images.<n>We validate DePT3R on several challenging benchmarks involving dynamic scenes, demonstrating strong performance and significant improvements in memory efficiency.
  arXiv  Detail & Related papers  (2025-12-15T09:21:28Z)
• 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)
• 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)
• Puppeteer: Rig and Animate Your 3D Models [105.11046762553121]
  Puppeteer is a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects.<n>Our system first predicts plausible skeletal structures via an auto-regressive transformer.<n>It then infers skinning weights via an attention-based architecture.
  arXiv  Detail & Related papers  (2025-08-14T17:59:31Z)
• MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion [118.74385965694694]
  We present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes.<n>By simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes.<n>We show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics.
  arXiv  Detail & Related papers  (2024-10-04T18:00:07Z)
• SWinGS: Sliding Windows for Dynamic 3D Gaussian Splatting [7.553079256251747]
  We extend 3D Gaussian Splatting to reconstruct dynamic scenes. We produce high-quality renderings of general dynamic scenes with competitive quantitative performance. Our method can be viewed in real-time in our dynamic interactive viewer.
  arXiv  Detail & Related papers  (2023-12-20T03:54:03Z)
• EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [85.17951804790515]
  EmerNeRF is a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. It simultaneously captures scene geometry, appearance, motion, and semantics via self-bootstrapping. Our method achieves state-of-the-art performance in sensor simulation.
  arXiv  Detail & Related papers  (2023-11-03T17:59:55Z)
• DOT: Dynamic Object Tracking for Visual SLAM [83.69544718120167]
  DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.
  arXiv  Detail & Related papers  (2020-09-30T18:36:28Z)

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.