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

VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion

This work introduces a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos.

OAHPE: occlusion-aware hybrid routing for efficient and robust 3D human pose estimation in mixed-occlusion videos.

Attention-mechanism-based methods for video-based 3D human pose estimation (HPE) have demonstrated strong performance in spatio-temporal modeling. However, the quadratic complexity introduced by attention leads to substantial computational and memory overhead during long-sequence inference, thereby hindering lightweight deployment. Inspired by the long-range modeling capability of the state space model (SSM) with linear complexity, we propose the Occlusion-Aware Hybrid Pose Estimation (OAHPE) model to address the dual challenges of high uncertainty in occluded frames and high redundancy in visible frames during 3D human pose estimation from video. This model employs a differentiated dual-path modeling strategy. For occluded segments, we integrate a multi-scale dilated convolutions with linear-complexity-based Mamba module, and employ a dual local-enhancement mechanism to capture fine-grained joint structures and long-range inter-joint dependencies at the full frame rate. For visible segments, we design an online selection mechanism to reduce temporal redundancy, and introduce a distillation-based temporal recovery objective to enforce dynamic consistency. Finally, predictions from the two paths are backfilled, aligned, and fused at the original sequence resolution. Comprehensive quantitative and qualitative evaluations on the Human3.6M and MPI-INF-3DHP benchmarks demonstrate that OAHPE achieves mean per-joint position errors of 43.5 mm and 28.5 mm, respectively, while reducing the model size and training memory footprint to 2.6 M parameters and 12.5 GB. These results indicate that the proposed method attains superior performance with substantially lower parameter and memory costs, achieving a favorable trade-off between pose estimation accuracy and computational efficiency.

Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation

This work proposes an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model with minimal architectural changes and consistently outperforms existing state-of-the-art approaches.

Progressive feature-space alignment for pose-controllable virtual try-on

Multiple Hypothesis Flow Estimation for Video Frame Interpolation under Matching Ambiguity

Many flow-based video frame interpolation (VFI) methods synthesize an intermediate frame by estimating optical flow fields, warping the two input frames, and blending the warped observations. These latent flow fields are typically learned through image-level reconstruction supervision without direct flow annotations. In ambiguous regions containing repetitive or stochastic textures, rotating symmetric structures, or fast motion with blur, the matching evidence for a single query may contain multiple comparable and spatially separated peaks. Although the ground-truth intermediate frame provides indirect supervision, it may not uniquely identify the latent correspondence in ambiguous regions.When several locations provide multiple plausible matches, a single-flow estimator can retain only one displacement and discard the remaining candidates. If the selected match is incorrect or inconsistent with those of neighboring pixels, warping samples content from mismatched locations, producing ghosting, structural distortion, or blur.To address this limitation, we propose a multiple hypothesis flow estimation framework that preserves top-K candidate correspondences and selects one per location through a reliability-guided router. Each hypothesis is initialized from a coarse matching anchor and refined separately through anchor-centered local attention. Frame synthesis is thus conditioned on one selected flow-appearance hypothesis rather than a soft combination of candidate motions.Experiments on the proposed MA-HD benchmark and public VFI benchmarks show that our method achieves the best LPIPS and DISTS among the compared methods.

Dynamic View Synthesis from Monocular Videos via Motion-aware Gaussian Splatting.

This paper tackles the challenge of novel view synthesis in complex scenes with under-constrained motion, as captured in monocular videos. Existing methods mainly focus on handling motion restricted within a bounded 3D volume, relying on spatio-temporal information to drive dynamic Gaussian deformations. However, due to the inherent motion ambiguities in monocular dynamic 3D representations and the limited observations, these methods face challenges in handling such scenes, often leading to incomplete geometry and boundary artifacts. To mitigate these issues, we propose a semantics-guided scene decoupling module that separates Gaussian primitives into static and dynamic components based on motion vectors. Further more, to enhance the capability in modeling non-rigid motions, we introduce a motion-aware densification module for motion compensation, which alleviates the incomplete rendering of dynamic objects caused by insufficient spatio-temporal information. Experimental results on real-world datasets demonstrate that our approach outperforms state-of-the-art methods in preserving both the integrity and detailed appearance of moving objects in dynamic scenes.