13 citations · 32 across the 14 of their papers we have counts for
14 papers
Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Zhenyu Zhou, Defang Chen, Can Wang +1
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number…
Customizing Synthetic Data for Data-Free Student Learning
Shiya Luo, Defang Chen, Can Wang
Data-free knowledge distillation (DFKD) aims to obtain a lightweight student model without original training data. Existing works generally synthesize data from the pre-trained tea…
Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning
Hailin Zhang, Defang Chen, Can Wang
Multi-Teacher knowledge distillation provides students with additional supervision from multiple pre-trained teachers with diverse information sources. Most existing methods explor…
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
Jingbo Zhang, Xiaoyu Li, Ziyu Wan +2
Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D…
AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control
Ruixiang Jiang, Can Wang, Jingbo Zhang +4
Neural implicit fields are powerful for representing 3D scenes and generating high-quality novel views, but it remains challenging to use such implicit representations for creating…
NeRF-Art: Text-Driven Neural Radiance Fields Stylization
Can Wang, Ruixiang Jiang, Menglei Chai +3
As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains chall…
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Wujie Sun, Defang Chen, Can Wang +3
Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distill…
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision
Jiongyu Guo, Defang Chen, Can Wang
Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data…
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks
Jiongyu Guo, Defang Chen, Can Wang
Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its…
Confidence-Aware Multi-Teacher Knowledge Distillation
Hailin Zhang, Defang Chen, Can Wang
Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training. To boost the student performance, some…
Knowledge Distillation with Deep Supervision
Shiya Luo, Defang Chen, Can Wang
Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model. However, in the tradi…
CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields
Can Wang, Menglei Chai, Mingming He +2
We present CLIP-NeRF, a multi-modal 3D object manipulation method for neural radiance fields (NeRF). By leveraging the joint language-image embedding space of the recent Contrastiv…
Deep Portrait Lighting Enhancement with 3D Guidance
Fangzhou Han, Can Wang, Hao Du +1
Despite recent breakthroughs in deep learning methods for image lighting enhancement, they are inferior when applied to portraits because 3D facial information is ignored in their…
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
Can Wang, Menglei Chai, Mingming He +2
Face image manipulation via three-dimensional guidance has been widely applied in various interactive scenarios due to its semantically-meaningful understanding and user-friendly c…