7 papers
Physics-Aware 3D Gaussian Editing for Driving Scene Generation
Feng Zhou, Jian Zhang, Yuhang Sun +5
3D Gaussian Splatting (3DGS) has shown great potential in autonomous driving simulation and data generation, enabling photorealistic reconstruction and flexible scene manipulation.…
A Tilted Seesaw: Revisiting Autoencoder Trade-off for Controllable Diffusion
Pu Cao, Yiyang Ma, Feng Zhou +3
In latent diffusion models, the autoencoder (AE) is typically expected to balance two capabilities: faithful reconstruction and a generation-friendly latent space (e.g., low gFID).…
Controllable Generation with Text-to-Image Diffusion Models: A Survey
Pu Cao, Feng Zhou, Qing Song +1
In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guid…
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
Yiyang Ma, Feng Zhou, Xuedan Yin +3
Leveraging pre-trained Diffusion Transformers (DiTs) for high-resolution (HR) image synthesis often leads to spatial layout collapse and degraded texture fidelity. Prior work mitig…
Preliminary Explorations with GPT-4o(mni) Native Image Generation
Pu Cao, Feng Zhou, Junyi Ji +8
Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition unde…
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
Pu Cao, Feng Zhou, Lu Yang +2
In-domain generation aims to perform a variety of tasks within a specific domain, such as unconditional generation, text-to-image, image editing, 3D generation, and more. Early res…