S-Lab for Advanced Intelligence

S-Lab for Advanced Intelligence, established in 2020, is a university laboratory at NTU focusing on research and development of cutting-edge AI technologies in computer vision, natural language processing, reinforcement learning, deep learning, and distributed computing. We aim to create impactful applications spanning various strategic areas in partnership with academic, industry, and government organizations.

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1 to 10 of 38 Results

SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (CVPR 2024) Jan 16, 2025 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...
FashionEngine: Interactive 3D Human Generation and Editing via Multimodal Controls Jan 16, 2025 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...
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment Jan 15, 2025 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...
Replication Data for: Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images Jan 10, 2025 Kong, Jiayi; Song, Xurui; Huai, Shuo; Xu, Baixin; Luo, Jun; He, Ying, 2025, "Replication Data for: Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images", https://doi.org/10.21979/N9/T3AGA8, DR-NTU (Data), V1 While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high-resolution multi-view images, posing notable privacy issues. Individuals are particularly sensitive to facial features, and facial image leakage can lead to many malic...
Learning 3D Garment Animation from Trajectories of A Piece of Cloth Jan 3, 2025 Shao, Yidi; Loy, Chen Change; Dai, Bo, 2025, "Learning 3D Garment Animation from Trajectories of A Piece of Cloth", https://doi.org/10.21979/N9/4YSCML, DR-NTU (Data), V1 Garment animation is ubiquitous in various applications, such as virtual reality, gaming, and film producing. Recently, learning-based approaches obtain compelling performance in animating diverse garments under versatile scenarios. Nevertheless, to mimic the deformations of the...
KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy Jan 3, 2025 Xu, Qianxiong; Long, Cheng; Li, Ziyue; Ruan, Sijie; Zhao, Rui; Li, Zhishuai, 2025, "KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy", https://doi.org/10.21979/N9/QH6QZN, DR-NTU (Data), V1 Sensors are commonly deployed to perceive the environment. However, due to the high cost, sensors are usually sparsely deployed. Kriging is the tailored task to infer the unobserved nodes (without sensors) using the observed nodes (with sensors). The essence of kriging task is tr...
I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models Nov 25, 2024 Ouyang, Wenqi; Dong, Yi; Yang, Lei; Si, Jianlou; Pan, Xingang, 2024, "I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models", https://doi.org/10.21979/N9/2ZLRYG, DR-NTU (Data), V1 The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the development of more diverse, high-quali...
Video Diffusion Models are Training-free Motion Interpreter and Controller Nov 7, 2024 Xiao, Zeqi; Zhou, Yifan; Yang, Shuai; Pan, Xingang, 2024, "Video Diffusion Models are Training-free Motion Interpreter and Controller", https://doi.org/10.21979/N9/HQM313, DR-NTU (Data), V1 Video generation primarily aims to model authentic and customized motion across frames, making understanding and controlling the motion a crucial topic. Most diffusion-based studies on video motion focus on motion customization with training-based paradigms, which, however, deman...
MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders Oct 23, 2024 Jiang, Xueying; Jin, Sheng; Zhang, Xiaoqin; Shao, Ling; Lu, Shijian, 2024, "MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders", https://doi.org/10.21979/N9/5ILJOM, DR-NTU (Data), V1 Monocular 3D object detection aims for precise 3D localization and identification of objects from a single-view image. Despite its recent progress, it often struggles while handling pervasive object occlusions that tend to complicate and degrade the prediction of object dimension...
Replication Data for: ReVersion: Diffusion-Based Relation Inversion from Images Oct 8, 2024 Huang, Ziqi; Wu, Tianxing; Jiang, Yuming; Chan, Kelvin C. K.; Liu, Ziwei, 2024, "Replication Data for: ReVersion: Diffusion-Based Relation Inversion from Images", https://doi.org/10.21979/N9/UWSAXU, DR-NTU (Data), V1 A replication of the ReVersion Benchmark, for the paper "ReVersion: Diffusion-Based Relation Inversion from Images".

SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering (CVPR 2024)

Jan 16, 2025

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...

FashionEngine: Interactive 3D Human Generation and Editing via Multimodal Controls

Jan 16, 2025

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...

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

Jan 15, 2025

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...

Replication Data for: Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images

Jan 10, 2025

Kong, Jiayi; Song, Xurui; Huai, Shuo; Xu, Baixin; Luo, Jun; He, Ying, 2025, "Replication Data for: Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images", https://doi.org/10.21979/N9/T3AGA8, DR-NTU (Data), V1

While 3D head reconstruction is widely used for modeling, existing neural reconstruction approaches rely on high-resolution multi-view images, posing notable privacy issues. Individuals are particularly sensitive to facial features, and facial image leakage can lead to many malic...

Learning 3D Garment Animation from Trajectories of A Piece of Cloth

Jan 3, 2025

Shao, Yidi; Loy, Chen Change; Dai, Bo, 2025, "Learning 3D Garment Animation from Trajectories of A Piece of Cloth", https://doi.org/10.21979/N9/4YSCML, DR-NTU (Data), V1

Garment animation is ubiquitous in various applications, such as virtual reality, gaming, and film producing. Recently, learning-based approaches obtain compelling performance in animating diverse garments under versatile scenarios. Nevertheless, to mimic the deformations of the...

KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy

Jan 3, 2025

Xu, Qianxiong; Long, Cheng; Li, Ziyue; Ruan, Sijie; Zhao, Rui; Li, Zhishuai, 2025, "KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy", https://doi.org/10.21979/N9/QH6QZN, DR-NTU (Data), V1

Sensors are commonly deployed to perceive the environment. However, due to the high cost, sensors are usually sparsely deployed. Kriging is the tailored task to infer the unobserved nodes (without sensors) using the observed nodes (with sensors). The essence of kriging task is tr...

I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models

Nov 25, 2024

Ouyang, Wenqi; Dong, Yi; Yang, Lei; Si, Jianlou; Pan, Xingang, 2024, "I2VEdit: First-Frame-Guided Video Editing via Image-to-Video Diffusion Models", https://doi.org/10.21979/N9/2ZLRYG, DR-NTU (Data), V1

The remarkable generative capabilities of diffusion models have motivated extensive research in both image and video editing. Compared to video editing which faces additional challenges in the time dimension, image editing has witnessed the development of more diverse, high-quali...

Video Diffusion Models are Training-free Motion Interpreter and Controller

Nov 7, 2024

Xiao, Zeqi; Zhou, Yifan; Yang, Shuai; Pan, Xingang, 2024, "Video Diffusion Models are Training-free Motion Interpreter and Controller", https://doi.org/10.21979/N9/HQM313, DR-NTU (Data), V1

Video generation primarily aims to model authentic and customized motion across frames, making understanding and controlling the motion a crucial topic. Most diffusion-based studies on video motion focus on motion customization with training-based paradigms, which, however, deman...

MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders

Oct 23, 2024

Jiang, Xueying; Jin, Sheng; Zhang, Xiaoqin; Shao, Ling; Lu, Shijian, 2024, "MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders", https://doi.org/10.21979/N9/5ILJOM, DR-NTU (Data), V1

Monocular 3D object detection aims for precise 3D localization and identification of objects from a single-view image. Despite its recent progress, it often struggles while handling pervasive object occlusions that tend to complicate and degrade the prediction of object dimension...

Replication Data for: ReVersion: Diffusion-Based Relation Inversion from Images

Oct 8, 2024

Huang, Ziqi; Wu, Tianxing; Jiang, Yuming; Chan, Kelvin C. K.; Liu, Ziwei, 2024, "Replication Data for: ReVersion: Diffusion-Based Relation Inversion from Images", https://doi.org/10.21979/N9/UWSAXU, DR-NTU (Data), V1

A replication of the ReVersion Benchmark, for the paper "ReVersion: Diffusion-Based Relation Inversion from Images".

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