32 citations · 246 across the 61 of their papers we have counts for
10 papers · 1 filter
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Yinghao Xu, Menglei Chai, Zifan Shi +8
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of obj…
Towards Smooth Video Composition
Qihang Zhang, Ceyuan Yang, Yujun Shen +2
Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video w…
GLeaD: Improving GANs with A Generator-Leading Task
Qingyan Bai, Ceyuan Yang, Yinghao Xu +3
Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes fr…
Deep Generative Models on 3D Representations: A Survey
Zifan Shi, Sida Peng, Yinghao Xu +3
Generative models aim to learn the distribution of observed data by generating new instances. With the advent of neural networks, deep generative models, including variational auto…
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
Zifan Shi, Yinghao Xu, Yujun Shen +3
3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. A popular solution is to adopt t…
Improving GANs with A Dynamic Discriminator
Ceyuan Yang, Yujun Shen, Yinghao Xu +3
Discriminator plays a vital role in training generative adversarial networks (GANs) via distinguishing real and synthesized samples. While the real data distribution remains the sa…