Aug 2026· IEEE Transactions on Visualization and Computer Graphics· Vol PP· 0 citations
Medicine
TL;DR
This paper proposes a semantics-guided scene decoupling module that separates Gaussian primitives into static and dynamic components based on motion vectors, and introduces a motion-aware densification module for motion compensation, which alleviates the incomplete rendering of dynamic objects caused by insufficient spatio-temporal information.
Abstract
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.
AniGS is presented, a method for scene-level animation of 3D Gaussian Splatting (3DGS) reconstructions that adds subtle, distributed dynamics, e.g., vegetation motion, while preserving rigid structures in reconstructed environments.
GrainGS is a dynamic Gaussian framework that combines a hierarchical anchor scaffold with per-Gaussian deformation that achieves high reconstruction quality, real-time novel view synthesis, and compact storage.
TaylorMoDe-GS, the first 3DGS framework tailored for multi-view dynamic object deblurring, shifts the modeling paradigm from displacement fitting to velocity driven modeling, and introduces a neural Peano remainder network to compensate for high frequency non-linear dynamics.
Xiaofeng Quan, Junzhe Wan, Chao Cai et al.· 0 citations
Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Existing methods typically address scene reconstruction and motion estimation separately: scene reconstruction methods ignore the wearer, whereas motion estimation methods lack explicit scene geometry and often depend on external trajectories. Joint recovery is challenging because the two tasks exhibit asymmetric visibility and require different prediction paradigms. The largely visible scene supports deterministic geometric regression, whereas the severely occluded body requires generative motion inference. We therefore propose RESELF (REconstructing the Scene and the sELF), a unified framework that couples deterministic metric geometry reconstruction with geometry-conditioned motion generation. RESELF adapts a geometry foundation model pre-trained on large-scale exocentric data to egocentric video using frame-wise scale and relative-pose consistency objectives. The resulting camera trajectory and latent geometric features condition a diffusion model that recovers the wearer's motion. A subsequent closed-loop kinematic feedback stage further refines the camera head while preserving the reconstructed scene geometry. To support training and evaluation, we curate EE4D-JSM from EgoExo4D by aligning egocentric video, sparse metric scene geometry, camera trajectories, and full-body motion annotations. Experiments show that RESELF outperforms state-of-the-art methods designed for the individual tasks across depth estimation, camera tracking, and full-body motion estimation. Code, models, and datasets will be available at https://ka1guan.github.io/RESELF/.
Kai Guan, Minchao Jiang, Ruichen WangLi et al.· 0 citations
Novel-view synthesis of dynamic scenes, crucial for AR/VR applications, remains a challenging problem. Recent methods adapt representations like 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) for dynamic scenes by incorporating time as the fourth dimension (4D representations). These 4D representations still suffer from aliasing artifacts, especially when generating novel views from divergent viewpoints (zoom-in/zoom-out operations). While using 3D smoothing filters like those proposed in Mip-Splatting might seem like a possible solution, they fail to account for local motion and also exhibit aliasing. To address this, we propose a motion-aware 3D smoothing filter specifically designed for 4D representations. Our approach adapts the filter strength based on local motion information, effectively mitigating aliasing without compromising rendering quality. This is achieved by estimating the joint density function of time and focal-to-depth ratio using a non-parametric estimation method. During inference, we sample from this joint distribution to determine the appropriate smoothing filter. This flexible strategy can be integrated with various 4D representations. Our evaluations on standard datasets demonstrate superior performance compared to state-of-the-art methods.
Ankit Dhiman, Kunal A Kathare, P.Marichamy M. Vignesh et al.· 0 citations
The integration of novel view synthesis (NVS) and open-vocabulary segmentation (OVS) has recently yielded powerful feed-forward 3D foundation models. However, their inherent reliance on static-scene assumptions leads to severe misalignment of spatial features in unconstrained dynamic environments. To bridge this critical gap, we propose SPAR, a novel joint semantic-geometric encoding architecture that explicitly isolates transient dynamic noise prior to latent space aggregation. Furthermore, we introduce a dynamic-region-aware end-to-end training paradigm that structurally couples motion estimation with multi-view visual and semantic learning. This unified approach enables the network to inherently resolve motion conflicts and distill multi-view consistent, temporally stable scene representations from dynamic inputs. Extensive experiments on the challenging D-RE10K benchmark demonstrate that SPAR achieves state-of-the-art performance. Our end-to-end approach achieves exceptional novel view synthesis quality, yielding a PSNR of 22.15 dB and 23.33 dB given only 3 and 4 input views respectively. Despite being trained in a self-supervised manner, our model achieves an mIoU of 88.5% for motion mask prediction. Furthermore, our analysis reveals a strong inter-task synergy between photometric scene reconstruction and semantic understanding, where semantic synthesis learning consistently enhances photometric fidelity in novel view rendering. Code will be available at https://github.com/dmucby/SPAR.
Boyu Cai, Li Yang, Yan Xu et al.· 0 citations
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