5 papers · 1 filter
CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds
Peng Zheng, Ruiqi Liu, Rui Ma +1
Recent subject-to-image models have achieved impressive progress in personalized image generation, yet they still struggle to preserve fine-grained subject-specific details. A majo…
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis
Peng Zheng, Junke Wang, Yi Chang +3
Recent advances in large language models (LLMs) have spurred interests in encoding images as discrete tokens and leveraging autoregressive (AR) frameworks for visual generation. Ho…
FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization
Peng Zheng, Ye Wang, Rui Ma +1
Subject-driven image generation plays a crucial role in applications such as virtual try-on and poster design. Existing approaches typically fine-tune pretrained generative models…
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis
Peng Zheng, Linzhi Huang, Yizhou Yu +3
Neural volume rendering techniques, such as NeRF, have revolutionized 3D-aware image synthesis by enabling the generation of images of a single scene or object from various camera…
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes
Jiaxing Zhao, Peng Zheng, Rui Ma
Creating large LiDAR datasets with pixel-level labeling poses significant challenges. While numerous data augmentation methods have been developed to reduce the reliance on manual…