  Recent 3D large reconstruction models (LRMs) can generate high-quality 3D
content in sub-seconds by integrating multi-view diffusion models with scalable
multi-view reconstructors. Current works further leverage 3D Gaussian Splatting
as 3D representation for improved visual quality and rendering efficiency.
However, we observe that existing Gaussian reconstruction models often suffer
from multi-view inconsistency and blurred textures. We attribute this to the
compromise of multi-view information propagation in favor of adopting powerful
yet computationally intensive architectures (e.g., Transformers). To address
this issue, we introduce MVGamba, a general and lightweight Gaussian
reconstruction model featuring a multi-view Gaussian reconstructor based on the
RNN-like State Space Model (SSM). Our Gaussian reconstructor propagates causal
context containing multi-view information for cross-view self-refinement while
generating a long sequence of Gaussians for fine-detail modeling with linear
complexity. With off-the-shelf multi-view diffusion models integrated, MVGamba
unifies 3D generation tasks from a single image, sparse images, or text
prompts. Extensive experiments demonstrate that MVGamba outperforms
state-of-the-art baselines in all 3D content generation scenarios with
approximately only $0.1\times$ of the model size.
