RecEdit-Drive is introduced, a framework that integrates Spatial Feature Warping and Spatiotemporal Collaborative Modeling to effectively control 3D object variations and enhance video consistency, and an inference strategy to reconstruct an accurate background structure through noise manipulation is designed.
Driving scene reconstruction and rendering, especially with 3D Gaussian Splatting, has become an important component of autonomous driving simulation. However, rendered views often degrade under extrapolated ego trajectories and scene edits, producing blurry structures, temporal flicker, and foreground-background misal...
The proposed MaskFlow, a training framework for precise localization, consistent background preservation, and seamless boundary transitions, incorporates the mask into the probability path and flow-matching objective, coordinating generation within the editable region with source preservation outside it.
Rui Xu, Yang Yong, Shun-Zi Yang et al.· 0 citations
Streaming 3D reconstruction demands both speed and temporal fidelity, goals that existing methods undermine by updating every Gaussian every frame, even in static regions. We present DecoGS, a method for efficient online training of 3D Gaussians from streaming videos. Unlike prior methods that update the entire scene i...
Idil Sulo, Alexey Supikov, Ilke Demir et al.· 0 citations
This work presents a progressive two-stage training framework that decouples geometry-aware foreground transformation from background preservation and realistic video composition, without mesh-pixel alignment and explicit 3D reconstruction at inference.
Youze Huang, Peng-Hui Ruan, Bojia Zi et al.· 0 citations
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 sp...
Chulin Zhao, Xue Wang, Guo-Qing Zhou et al.· IEEE Transactions on Visuali...· 0 citations
Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically d...
Zhiyuan Li, Chi-Man Pun, Peng-Tao Jiang et al.· 0 citations
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