Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction

• URL: http://arxiv.org/abs/2606.29230v1
• Date: Sun, 28 Jun 2026 06:37:50 GMT
• Title: Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction
• Abstract summary: We propose a simple yet effective framework called Anchor-guided adaptive inter-frame motion cues propagating (Again-Pose)<n>Again-Pose reformulating pose estimation in degraded frames as a motion-guided recovery task.<n>Experiments on standard benchmarks (Human3.6M, 3DPW, PoseTrack) and the challenging FineDiving dataset demonstrate that Again-Pose significantly outperforms state-of-the-art methods in robustness and stability.
• Score: 4.345128976485766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstructing continuous 3D human poses from unconstrained videos is challenging, especially in extreme motion scenarios involving severe motion blur and occlusion. Current state-of-the-art methods typically rely on implicit temporal attention to aggregate features across frames. However, under severe visual degradation, input features often suffer from collapse, rendering them indistinguishable from noise. In such cases, implicit aggregation fails to distinguish valid signals, leading to catastrophic reconstruction errors. To address this robustness gap, we propose a simple yet effective framework called Anchor-guided adaptive inter-frame motion cues propagating (Again-Pose), reformulating pose estimation in degraded frames as a motion-guided recovery task. Instead of blindly smoothing features, we explicitly identify high-quality Anchor Frames based on feature saliency and propagate reliable kinematic cues to "inpaint" the poses of degraded intermediate frames. Specifically, a Dual-path Motion-aware Module captures fine-grained inter-frame dynamics, while a Difference-weighted Fusion Module adaptively propagates these cues to suppress drift. Extensive experiments on standard benchmarks (Human3.6M, 3DPW, PoseTrack) and the challenging FineDiving dataset demonstrate that Again-Pose significantly outperforms state-of-the-art methods in robustness and stability, effectively recovering plausible poses where other methods fail.

Related papers

• DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation [58.47973015036709]
  DisPOSE is a self-supervised framework that approximates the inherently discrete multi-view person-assignment problem.<n>By employing differentiable Sinkhorn projections during denoising, our model learns to guide solutions toward valid and feasible assignments.<n>The proposed approach outperforms current state-of-the-art self-supervised methods on standard datasets.
  arXiv  Detail & Related papers  (2026-06-05T16:14:39Z)
• LoCAtion: Long-time Collaborative Attention Framework for High Dynamic Range Video Reconstruction [17.88716377235245]
  We propose LoCAtion, a framework that reformulates HDR video generation from a fragile spatial warping task into a robust, alignment-free collaborative feature routing problem.<n>LoCAtion achieves state-of-the-art visual quality and temporal stability, offering a highly competitive balance between accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2026-03-15T13:34:46Z)
• Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting [17.957935001501106]
  We present Mango-GS, a node-guided framework for high-fidelity 4D reconstruction.<n>We leverage a temporal Transformer to model motion dependencies within a short window of frames.<n>Mango-GS achieves state-of-the-art reconstruction quality and real-time rendering speed.
  arXiv  Detail & Related papers  (2026-03-12T05:00:16Z)
• GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry [61.24189040578178]
  We propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms.<n>Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion.<n>Our approach achieves state-of-the-art motion segmentation performance with high efficiency.
  arXiv  Detail & Related papers  (2026-02-25T11:36:33Z)
• GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering [54.489285024494855]
  Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent.<n>Existing approaches, depending on the domain they operate, suffer from several issues that degrade the user experience.<n>We introduce textbfGaVS, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent local reconstruction and rendering' paradigm.
  arXiv  Detail & Related papers  (2025-06-30T15:24:27Z)
• HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene [24.789092424634536]
  We propose HAIF-GS, a unified framework that enables structured and consistent dynamic modeling through sparse anchor-driven deformation.<n>We show that HAIF-GS significantly outperforms prior dynamic 3DGS methods in rendering quality, temporal coherence, and reconstruction efficiency.
  arXiv  Detail & Related papers  (2025-06-11T08:45:08Z)
• Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
  Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts.<n>Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion.<n>We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
  arXiv  Detail & Related papers  (2025-03-25T17:58:48Z)
• RIGID: Recurrent GAN Inversion and Editing of Real Face Videos [73.97520691413006]
  GAN inversion is indispensable for applying the powerful editability of GAN to real images. Existing methods invert video frames individually often leading to undesired inconsistent results over time. We propose a unified recurrent framework, named textbfRecurrent vtextbfIdeo textbfGAN textbfInversion and etextbfDiting (RIGID) Our framework learns the inherent coherence between input frames in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-08-11T12:17:24Z)
• Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation [38.571715193347366]
  We present a novel hierarchical alignment framework for multi-frame human pose estimation. We rank No.1 in the Multi-frame Person Pose Estimation Challenge on benchmark dataset PoseTrack 2017, and obtain state-of-the-art performance on benchmarks Sub-JHMDB and Pose-Track 2018.
  arXiv  Detail & Related papers  (2022-03-29T04:29:16Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)
• Deep Dual Consecutive Network for Human Pose Estimation [44.41818683253614]
  We propose a novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection. Our method ranks No.1 in the Multi-frame Person Pose Challenge Challenge on the large-scale benchmark datasets PoseTrack 2017 and PoseTrack 2018.
  arXiv  Detail & Related papers  (2021-03-12T13:11:27Z)

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.