31 to 40 of 84 Results
Feb 5, 2025
Lan, Yushi; Zhou, Shangchen; Lyu, Zhaoyang; Hong, Fangzhou; Yang, Shuai; Dai, Bo; Pan, Xingang; Loy, Chen Change, 2025, "GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation", https://doi.org/10.21979/N9/ZQ85KI, DR-NTU (Data), V1
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quali... |
Feb 4, 2025
Xiao, Zeqi; Ouyang, Wenqi; Zhou, Yifan; Yang, Shuai; Yang, Lei; Si, Jianlou; Pan, Xingang, 2025, "Trajectory attention for fine-grained video motion control", https://doi.org/10.21979/N9/II0EM4, DR-NTU (Data), V1
Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention... |
Feb 4, 2025
Liao, Kang; Yue, Zongsheng; Wang, Zhouxia; Loy, Chen Change, 2025, "Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration", https://doi.org/10.21979/N9/DMB2QK, DR-NTU (Data), V1
Although learning-based image restoration methods have made significant progress, they still struggle with limited generalization to real-world scenarios due to the substantial domain gap caused by training on synthetic data. Existing methods address this issue by improving data... |
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... |
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... |
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... |
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... |
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... |
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... |
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... |
