Recent advancements in text-to-3D generation technology have significantly advanced the
conversion of textual descriptions into imaginative well-geometrical and finely textured 3D …

We present Stable Video 4D (SV4D), a latent video diffusion model for multi-frame and multi-
view consistent dynamic 3D content generation. Unlike previous methods that rely on …

By leveraging the text-to-image diffusion prior, score distillation can synthesize 3D contents
without paired text-3D training data. Instead of spending hours of online optimization per text …

Large scale 3D scene reconstruction is important for applications such as virtual reality and
simulation. Existing neural rendering approaches (eg, NeRF, 3DGS) have achieved realistic …

Recent 3D large reconstruction models typically employ a two-stage process, including first
generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward …

In this work, we introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an
approach which can predict high-quality assets with 512k Gaussians and 21 input images in …

We introduce MeshAnything V2, an autoregressive transformer that generates Artist-Created
Meshes (AM) aligned to given shapes. It can be integrated with various 3D asset production …

Reconstructing and rendering 3D objects from highly sparse views is of critical importance
for promoting applications of 3D vision techniques and improving user experience …

The increasing demand for high-quality 3D assets across various industries necessitates
efficient and automated 3D content creation. Despite recent advancements in 3D generative …

We propose the Large View Synthesis Model (LVSM), a novel transformer-based approach
for scalable and generalizable novel view synthesis from sparse-view inputs. We introduce …