72 citations · 83 across the 10 of their papers we have counts for
10 papers
RapidMV: Leveraging Spatio-Angular Representations for Efficient and Consistent Text-to-Multi-View Synthesis
Seungwook Kim, Yichun Shi, Kejie Li +2
Generating synthetic multi-view images from a text prompt is an essential bridge to generating synthetic 3D assets. In this work, we introduce RapidMV, a novel text-to-multi-view g…
Dual Diffusion for Unified Image Generation and Understanding
Zijie Li, Henry Li, Yichun Shi +4
Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-langua…
MVLight: Relightable Text-to-3D Generation via Light-conditioned Multi-View Diffusion
Dongseok Shim, Yichun Shi, Kejie Li +2
Recent advancements in text-to-3D generation, building on the success of high-performance text-to-image generative models, have made it possible to create imaginative and richly te…
SeedEdit: Align Image Re-Generation to Image Editing
Yichun Shi, Peng Wang, Weilin Huang
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance betwe…
Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation
Seungwook Kim, Yichun Shi, Kejie Li +2
Using image as prompts for 3D generation demonstrate particularly strong performances compared to using text prompts alone, for images provide a more intuitive guidance for the 3D…
HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
Mude Hui, Siwei Yang, Bingchen Zhao +5
This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feed…
CorrespondentDream: Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Seungwook Kim, Kejie Li, Xueqing Deng +3
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e.g., the Janus face problem or the content drift problem, in ze…
ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation
Peng Wang, Yichun Shi
We introduce "ImageDream," an innovative image-prompt, multi-view diffusion model for 3D object generation. ImageDream stands out for its ability to produce 3D models of higher qua…
Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Jianglong Ye, Peng Wang, Kejie Li +2
Zero-shot novel view synthesis (NVS) from a single image is an essential problem in 3D object understanding. While recent approaches that leverage pre-trained generative models can…
MVDream: Multi-view Diffusion for 3D Generation
Yichun Shi, Peng Wang, Jianglong Ye +3
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt. Learning from both 2D and 3D data, a multi-view diffusion mo…