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From the 1 of 13 linked papers with an AI index.

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13 papers

cs.CV2026

CLEAR: Conflict-aware Learning via Evidence-guided Adaptive Routing for Unified Sparse-View 3D Gaussian Super-Resolution

Hantang Li, Qiang Zhu, Xiandong Meng +2

Sparse-view 3D Gaussian Splatting Super-resolution is highly challenging since the sparse and low-resolution (LR) inputs lack sufficient geometric and high-frequency information fo…

cs.CV2026

T3HG-Editor: Text-driven 3D Human Garment Editing with Body Priors Embedded in SMPL-X

Shaoru Sun, Xingtao Wang, Zihan Ma +4

The paper introduces T3HG-Editor, a text-driven system for editing 3D human garments that leverages SMPL-X body priors to achieve high-fidelity and consistent results using Gaussia…

cs.AI2026

Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards

Xuehui Yu, Fucheng Cai, Meiyi Wang +2

Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provi…

cs.CV2026

NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results

Shuhong Liu, Chenyu Bao, Ziteng Cui +103

This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…

cs.CV2026

DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting

Hantang Li, Qiang Zhu, Xiandong Meng +2

Sparse-view reconstruction with 3D Gaussian Splatting (3DGS) is fundamentally ill-posed due to insufficient geometric supervision, often leading to severe overfitting and the emerg…

cs.CV2026

MTLSI-Net: A Linear Semantic Interaction Network for Parameter-Efficient Multi-Task Dense Prediction

Chen Liu, Hengyu Man, Xiaopeng Fan +1

Multi-task dense prediction aims to perform multiple pixel-level tasks simultaneously. However, capturing global cross-task interactions remains non-trivial due to the quadratic co…