27 citations · 36 across the 9 of their papers we have counts for
10 papers
EvalVerse: Pipeline-Aware and Expert-Calibrated Benchmarking for Professional Cinematic Video Generation
Songlin Yang, Haobin Zhong, Ruilin Zhang +23
The rapid evolution of generative video foundation models has propelled the field toward professional-grade cinematic synthesis. To achieve such demanding quality, the community tr…
WavFlow: Audio Generation in Waveform Space
Feiyan Zhou, Luyuan Wang, Shoufa Chen +6
Modern audio generation predominantly relies on latent-space compression, introducing additional complexity and potential information loss. In this work, we challenge this paradigm…
FreeCus: Free Lunch Subject-driven Customization in Diffusion Transformers
Yanbing Zhang, Zhe Wang, Qin Zhou +1
In light of recent breakthroughs in text-to-image (T2I) generation, particularly with diffusion transformers (DiT), subject-driven technologies are increasingly being employed for…
Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data
Mengping Yang, Zhe Wang, Ziqiu Chi +2
Generative adversarial networks (GANs) have made remarkable achievements in synthesizing images in recent years. Typically, training GANs requires massive data, and the performance…
Attention Calibration for Disentangled Text-to-Image Personalization
Yanbing Zhang, Mengping Yang, Qin Zhou +1
Recent thrilling progress in large-scale text-to-image (T2I) models has unlocked unprecedented synthesis quality of AI-generated content (AIGC) including image generation, 3D and v…
Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation
Mengping Yang, Zhe Wang, Wenyi Feng +2
Few-shot image generation, which aims to produce plausible and diverse images for one category given a few images from this category, has drawn extensive attention. Existing approa…
Multi-Scale And Token Mergence: Make Your ViT More Efficient
Zhe Bian, Zhe Wang, Wenqiang Han +1
Since its inception, Vision Transformer (ViT) has emerged as a prevalent model in the computer vision domain. Nonetheless, the multi-head self-attention (MHSA) mechanism in ViT is…
Image Synthesis under Limited Data: A Survey and Taxonomy
Mengping Yang, Zhe Wang
Deep generative models, which target reproducing the given data distribution to produce novel samples, have made unprecedented advancements in recent years. Their technical breakth…
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data
Mengping Yang, Zhe Wang, Ziqiu Chi +1
Training GANs under limited data often leads to discriminator overfitting and memorization issues, causing divergent training. Existing approaches mitigate the overfitting by emplo…
WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation
Mengping Yang, Zhe Wang, Ziqiu Chi +1
Existing few-shot image generation approaches typically employ fusion-based strategies, either on the image or the feature level, to produce new images. However, previous approache…