9,031 to 9,040 of 9,060 Results
Tabular Data - 6.3 MB - 90 Variables, 4090 Observations - UNF:6:lJhbFzGOX54xXRVoAlDWZA==
|
Tabular Data - 75.9 MB - 90 Variables, 49255 Observations - UNF:6:SbrLPqG1YAlx+puKKXhKFA==
|
Tabular Data - 2.0 MB - 91 Variables, 1290 Observations - UNF:6:b9kbj83NTWrbtx5rOBa9yA==
|
Tabular Data - 141.0 MB - 6138 Variables, 1290 Observations - UNF:6:hQcRMmsQai3PKw1d6fjDsQ==
|
Tabular Data - 36.1 MB - 1557 Variables, 1290 Observations - UNF:6:JtCmO01Q3JTU/BrMHG7Z/w==
|
Jan 16, 2025 - S-Lab for Advanced Intelligence
Hu, Tao; Hong, Fangzhou; Liu, Ziwei, 2025, "SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (CVPR 2024)", https://doi.org/10.21979/N9/JDZOJE, DR-NTU (Data), V1
Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However, existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relati... |
Jan 16, 2025 - S-Lab for Advanced Intelligence
Hu, Tao; Hong, Fangzhou; Chen, Zhaoxi; Liu, Ziwei, 2025, "FashionEngine: Interactive 3D Human Generation and Editing via Multimodal Controls", https://doi.org/10.21979/N9/WRPWAN, DR-NTU (Data), V1
We present FashionEngine, an interactive 3D human generation and editing system that creates 3D digital humans via user-friendly multimodal controls such as natural languages, visual perceptions, and hand-drawing sketches. FashionEngine automates the 3D human production with thre... |
Jan 15, 2025 - Sulfikar AMIR
Amir, Sulfikar, 2025, "Survey on Social Trust in AI", https://doi.org/10.21979/N9/OQF6TW, DR-NTU (Data), V1
This dataset results from a cross-country survey in East Asia |
Jan 15, 2025 -
Survey on Social Trust in AI
Comma Separated Values - 6.6 MB -
MD5: 5cfb87a3c625212fd898c41a34578370
|
Jan 15, 2025 - S-Lab for Advanced Intelligence
Liu, Chenxi; Xu, Qianxiong; Miao, Hao; Yang, Sun; Zhang, Lingzheng; Long, Cheng; Li, Ziyue; Zhao, Rui, 2025, "TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment", https://doi.org/10.21979/N9/V1XDVB, DR-NTU (Data), V1
Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series. Existing statistical and deep learning-based methods suffer from limited learnable parameters and small-scale training data. Recently, large language models... |
