1 citations · 1 across the 1 of their papers we have counts for
7 papers · 1 filter
Science-T2I: Addressing Scientific Illusions in Image Synthesis
Jialuo Li, Wenhao Chai, Xingyu Fu +2
Current image generation models produce visually compelling but scientifically implausible images, exposing a fundamental gap between visual fidelity and physical realism. In this…
Next-Embedding Prediction Makes Strong Vision Learners
Sihan Xu, Ziqiao Ma, Wenhao Chai +5
Inspired by the success of generative pretraining in natural language, we ask whether the same principles can yield strong self-supervised visual learners. Instead of training mode…
On Scaling Up 3D Gaussian Splatting Training
Hexu Zhao, Haoyang Weng, Daohan Lu +4
3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a sing…
BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
Jiacheng Chen, Ramin Mehran, Xuhui Jia +2
We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compos…
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
Wenhao Chai, Enxin Song, Yilun Du +6
Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation.…
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Nanye Ma, Shangyuan Tong, Haolin Jia +8
Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…