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20182026
most citedAnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

85 citations · 309 across the 51 of their papers we have counts for

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Showing 2022 · cs.CVShow all

6 papers · 2 filters

cs.CV2022★ 1 cited

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…

cs.CV2022★ 2 cited

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…

cs.CV2022

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…

cs.CV2022★ 12 cited

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…

cs.CV2022★ 5 cited

Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

Zhengkai Jiang, Yuxi Li, Ceyuan Yang +4

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Ada…

cs.CV2022★ 37 cited

Accelerating Diffusion Models via Early Stop of the Diffusion Process

Zhaoyang Lyu, Xudong XU, Ceyuan Yang +2

Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data d…