SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting
  Synthesis
Authors: name: Hyojun Go, name: Byeongjun Park, name: Jiho Jang, name: Jin-Young Kim, name: Soonwoo Kwon, name: Changick Kim

Text-based generation and editing of 3D scenes hold significant potential for
streamlining content creation through intuitive user interactions. While recent
advances leverage 3D Gaussian Splatting (3DGS) for high-fidelity and real-time
rendering, existing methods are often specialized and task-focused, lacking a
unified framework for both generation and editing. In this paper, we introduce
SplatFlow, a comprehensive framework that addresses this gap by enabling direct
3DGS generation and editing. SplatFlow comprises two main components: a
multi-view rectified flow (RF) model and a Gaussian Splatting Decoder
(GSDecoder). The multi-view RF model operates in latent space, generating
multi-view images, depths, and camera poses simultaneously, conditioned on text
prompts, thus addressing challenges like diverse scene scales and complex
camera trajectories in real-world settings. Then, the GSDecoder efficiently
translates these latent outputs into 3DGS representations through a
feed-forward 3DGS method. Leveraging training-free inversion and inpainting
techniques, SplatFlow enables seamless 3DGS editing and supports a broad range
of 3D tasks-including object editing, novel view synthesis, and camera pose
estimation-within a unified framework without requiring additional complex
pipelines. We validate SplatFlow's capabilities on the MVImgNet and DL3DV-7K
datasets, demonstrating its versatility and effectiveness in various 3D
generation, editing, and inpainting-based tasks.