DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
This addresses the challenge of controllable 3D object generation for computer vision and graphics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of generating controllable novel views for 3D objects from single images by proposing DreamComposer, a framework that enhances existing diffusion models with multi-view conditions, resulting in high-fidelity images for applications like 3D reconstruction.
Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views. In this paper, we present DreamComposer, a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically, DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then, it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis, further enhancing them to generate high-fidelity novel view images with multi-view conditions, ready for controllable 3D object reconstruction and various other applications.