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1,301 to 1,310 of 5,121 Results
Arbitrary-steps Image Super-resolution via Diffusion Inversion
Mar 11, 2025 - S-Lab for Advanced Intelligence
Yue, Zongsheng; Liao, Kang; Loy, Chen Change, 2025, "Arbitrary-steps Image Super-resolution via Diffusion Inversion", https://doi.org/10.21979/N9/SZJQME, DR-NTU (Data), V1
This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an int...
MatAnyone: Stable Video Matting with Consistent Memory Propagation
Mar 10, 2025 - S-Lab for Advanced Intelligence
Yang, Peiqing; Zhou, Shangchen; Zhao, Jixin; Tao, Qingyi; Loy, Chen Change, 2025, "MatAnyone: Stable Video Matting with Consistent Memory Propagation", https://doi.org/10.21979/N9/EN6LQI, DR-NTU (Data), V1
Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To tackle this, we propose MatAnyone, a practical framework designed for target-assigned video matting. Specifically, building on a memory-based fr...
3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
Mar 10, 2025 - S-Lab for Advanced Intelligence
Luo, Yihang; Zhou, Shangchen; Lan, Yushi; Pan, Xingang; Loy, Chen Change, 2025, "3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement", https://doi.org/10.21979/N9/3ARCLF, DR-NTU (Data), V1
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view consistency. In this study, we pr...
3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion
Mar 10, 2025 - S-Lab for Advanced Intelligence
Chen, Zhaoxi; Tang, Jiaxiang; Dong, Yuhao; Cao, Ziang; Hong, Fangzhou; Lan, Yushi; Wang,Tengfei; Xie, Haozhe; Wu, Tong; Saito, Shunsuke; Pan, Liang; Lin, Dahua; Liu, Ziwei, 2025, "3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion", https://doi.org/10.21979/N9/VMPHUZ, DR-NTU (Data), V1
The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the la...
Generative Gaussian Splatting for Unbounded 3D City Generation
Mar 10, 2025 - S-Lab for Advanced Intelligence
Xie, Haozhe; Chen, Zhaoxi; Hong, Fangzhou; Liu, Ziwei, 2025, "Generative Gaussian Splatting for Unbounded 3D City Generation", https://doi.org/10.21979/N9/JMQHVG, DR-NTU (Data), V1
3D city generation with NeRF-based methods shows promising generation results but is computationally inefficient. Recently 3D Gaussian Splatting (3D-GS) has emerged as a highly efficient alternative for object-level 3D generation. However, adapting 3D-GS from finite-scale 3D obje...
Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach
Feb 28, 2025 - Yuanjian LI
Li, Yuanjian; Madhukumar, A. S., 2025, "Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach", https://doi.org/10.21979/N9/HOX79X, DR-NTU (Data), V1
Python source code associated with the publication titled "Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach". These codes can be used to produce simulation figures in this publication.
BL_ChannelEstimation.py
Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach
Python Source Code - 20.8 KB - MD5: 8385c7d44b83e9f22e66b1d9d636c3ff
dicLearning.py
Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach
Python Source Code - 24.6 KB - MD5: 272737c4d886921be6c808b3eeed51a1
Environment.py
Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach
Python Source Code - 34.5 KB - MD5: 8cbb12fcbbe3f563ea287f67da9a00b6
main_BLCE_NMSE_vs_pilotLength.py
Feb 28, 2025 - Related Data for: Hybrid Near- and Far-Field THz UM-MIMO Channel Estimation: A Sparsifying Matrix Learning-Aided Bayesian Approach
Python Source Code - 5.4 KB - MD5: add33ace4acfbdf1659332c14f7ed543
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