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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.CV2026

MVFusion-GS: Motion-Variance Guided Temporal Attention for High-Quality Dynamic Gaussian Splatting

Jianwei Hu, Tingxuan Huang, Hengyu Zhou +4

The paper introduces MVFusion-GS, which adds motion‑variance guided refinement and a transformer‑based temporal attention module to deformation networks for dynamic 3D Gaussian spl…

cs.CV2026

CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID

Fengchun Zhang, Qiang Ma, Liuyu Xiang +3

Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains su…

cs.CV2026

Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting

Tingxuan Huang, Haowei Zhu, Jun-hai Yong +2

Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains a significant challenge. Existing Gaussian splatting approaches for dynamic scene…

cs.CV2026

TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

Haowei Zhu, Tingxuan Huang, Xing Wang +7

Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (T…

cs.CV2024

Mask-adaptive Gated Convolution and Bi-directional Progressive Fusion Network for Depth Completion

Tingxuan Huang, Jiacheng Miao, Shizhuo Deng +2

Depth completion is a critical task for handling depth images with missing pixels, which can negatively impact further applications. Recent approaches have utilized Convolutional N…

cs.CV2024

DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generation

Wei Wu, Xi Guo, Weixuan Tang +4

Recent advancements in generative models have provided promising solutions for synthesizing realistic driving videos, which are crucial for training autonomous driving perception m…