5 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…
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…
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…
CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model
Xiaoding Yuan, Shitao Tang, Kejie Li +2
This paper introduces Camera-free Diffusion (CamFreeDiff) model for 360-degree image outpainting from a single camera-free image and text description. This method distinguishes its…
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…