title: SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting
    Synthesis
summary: 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.
paper_authors._id: 6745466156ed6a63d03086b0
paper_authors.name: Hyojun Go
paper_authors._id: 6745466156ed6a63d03086b1
paper_authors.name: Byeongjun Park
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paper_authors.fullname: Byeongjun Park
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paper_authors.user: byeongjun-park
paper_authors._id: 6745466156ed6a63d03086b2
paper_authors.name: Jiho Jang
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paper_authors.fullname: Jiho Jang
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paper_authors.user: hagiss
paper_authors._id: 6745466156ed6a63d03086b3
paper_authors.name: Jin-Young Kim
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paper_authors._id: 65392959899a7547c963e133
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paper_authors.fullname: Jin-Young Kim
paper_authors.type: user
paper_authors.user: Jiny123
paper_authors._id: 6745466156ed6a63d03086b4
paper_authors.name: Soonwoo Kwon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:59:34.759Z
paper_authors._id: 65000be11e14749e84e6acac
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paper_authors.fullname: Soonwoo Kwon
paper_authors.type: user
paper_authors.user: snoolimal
paper_authors._id: 6745466156ed6a63d03086b5
paper_authors.name: Changick Kim
paper_title: SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting
    Synthesis
paper_summary: 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.
paper_ai_keywords: 3D Gaussian Splatting (3DGS) real-time rendering multi-view rectified flow (RF) model Gaussian Splatting Decoder (GSDecoder) latent space feed-forward 3DGS method training-free inversion inpainting techniques MVImgNet DL3DV-7K datasets