14 citations · 49 across the 24 of their papers we have counts for
20 papers · 1 filter
Infinite Worlds with Versatile Interactions
Zelin Gao, Qiuyu Wang, Jiapeng Zhu +17
We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded in…
AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
Haobo Li, Yanhong Zeng, Yunhong Lu +6
We present AAD-1, an Asymmetric Adversarial Distillation framework for One-step autoregressive image-to-video generation. State-of-the-art methods adopt adversarial distillation bu…
CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
Yihao Meng, Zichen Liu, Hao Ouyang +11
Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single scene; it requires progressing…
Advancing Open-source World Models
Robbyant Team, Zelin Gao, Qiuyu Wang +21
We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It…
Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
Yunhong Lu, Yanhong Zeng, Haobo Li +9
Efficient streaming video generation is critical for simulating interactive and dynamic worlds. Existing methods distill few-step video diffusion models with sliding window attenti…
UCD: Unconditional Discriminator Promotes Nash Equilibrium in GANs
Mengfei Xia, Nan Xue, Jiapeng Zhu +1
Adversarial training turns out to be the key to one-step generation, especially for Generative Adversarial Network (GAN) and diffusion model distillation. Yet in practice, GAN trai…
Learning Visual Generative Priors without Text
Shuailei Ma, Kecheng Zheng, Ying Wei +7
Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that gra…
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Jiapeng Zhu, Yujun Shen, Yinghao Xu +3
Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, apply…
Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis
Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng +3
Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis. Sparsely-activated mixtur…
Learning Modulated Transformation in GANs
Ceyuan Yang, Qihang Zhang, Yinghao Xu +3
The success of style-based generators largely benefits from style modulation, which helps take care of the cross-instance variation within data. However, the instance-wise stochast…
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs
Yinghao Xu, Yujun Shen, Jiapeng Zhu +2
Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and r…
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
Jiapeng Zhu, Ceyuan Yang, Yujun Shen +4
This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image. Establishing…
Interpreting Class Conditional GANs with Channel Awareness
Yingqing He, Zhiyi Zhang, Jiapeng Zhu +2
Understanding the mechanism of generative adversarial networks (GANs) helps us better use GANs for downstream applications. Existing efforts mainly target interpreting unconditiona…
High-fidelity GAN Inversion with Padding Space
Qingyan Bai, Yinghao Xu, Jiapeng Zhu +3
Inverting a Generative Adversarial Network (GAN) facilitates a wide range of image editing tasks using pre-trained generators. Existing methods typically employ the latent space of…
3D-Aware Indoor Scene Synthesis with Depth Priors
Zifan Shi, Yujun Shen, Jiapeng Zhu +2
Despite the recent advancement of Generative Adversarial Networks (GANs) in learning 3D-aware image synthesis from 2D data, existing methods fail to model indoor scenes due to the…
Region-Based Semantic Factorization in GANs
Jiapeng Zhu, Yujun Shen, Yinghao Xu +2
Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attribut…
One-Shot Generative Domain Adaptation
Ceyuan Yang, Yujun Shen, Zhiyi Zhang +4
This work aims at transferring a Generative Adversarial Network (GAN) pre-trained on one image domain to a new domain referring to as few as just one target image. The main challen…
Low-Rank Subspaces in GANs
Jiapeng Zhu, Ruili Feng, Yujun Shen +4
The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze…
Generative Hierarchical Features from Synthesizing Images
Yinghao Xu, Yujun Shen, Jiapeng Zhu +2
Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from s…
In-Domain GAN Inversion for Real Image Editing
Jiapeng Zhu, Yujun Shen, Deli Zhao +1
Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is diffi…