1 citations · 3 across the 5 of their papers we have counts for
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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…
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…