Multi View Diffusion
Multi-view diffusion models are revolutionizing 3D content generation by synthesizing multiple consistent views of an object from limited input (e.g., a single image or text prompt), enabling subsequent high-fidelity 3D reconstruction. Current research emphasizes improving the quality, consistency, and efficiency of these models, often employing architectures like transformers and diffusion models, sometimes coupled with Gaussian splatting for efficient 3D representation. This work has significant implications for various fields, including computer graphics, virtual and augmented reality, and digital asset creation, by offering faster and more realistic 3D content generation from diverse input sources.
Papers
CAT3D: Create Anything in 3D with Multi-View Diffusion Models
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron, Ben Poole
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion
Xinyang Li, Zhangyu Lai, Linning Xu, Jianfei Guo, Liujuan Cao, Shengchuan Zhang, Bo Dai, Rongrong Ji