SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis (2411.16443v3)

Abstract: 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.

• The paper presents SplatFlow, a unified framework that jointly handles 3D generation and editing through a multi-view rectified flow model and a Gaussian Splatting Decoder.
• It employs a simplified linear rectified flow with Stable Diffusion 3 guidance to efficiently produce multi-view images, depth maps, and camera poses, achieving significant gains in FID and CLIP scores.
• SplatFlow enhances real-time 3D scene workflows, offering actionable benefits for VR, gaming, and robotics by streamlining 3D content synthesis and manipulation.

Analysis of "SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis"

The paper under examination, "SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis," introduces SplatFlow, a framework designed to address the challenges and limitations of existing methods in text-based generation and editing of 3D scenes. This paper positions itself in the ongoing discourse of leveraging 3D Gaussian Splatting (3DGS) for real-time high-fidelity rendering, setting itself apart by proposing a unified framework capable of simultaneous 3D generation and editing—tasks that previous approaches treated separately.

Core Contributions

The primary contribution of the paper is the introduction of the SplatFlow framework, which consists of two core components: the multi-view rectified flow (RF) model and the Gaussian Splatting Decoder (GSDecoder). The RF model operates in latent space and is responsible for generating multi-view images, depths, and camera poses simultaneously from text prompts. This represents a significant step in addressing the diverse scene scales and intricate camera trajectories typical in real-world applications. The GSDecoder translates these latent outputs into 3DGS representations using a feed-forward method, optimizing the synthesis process.

Methodological Insights

The paper employs Rectified Flows (RF), diverging from traditional curved forward processes and opting for a simplified linear path, which theoretically offers a more computationally efficient sampling method. Training the neural network with a flow-matching objective aligns with the goals of reducing computational load while maintaining consistency in multi-view outputs. Furthermore, the paper's integration of Stable Diffusion 3 (SD3) guidance into the multi-view RF model to improve generation quality is a noteworthy innovation. This cross-model strategy enhances the flexibility and potential adaptability of SplatFlow to various generative model environments.

Experimental Validation

The authors validate SplatFlow on the MVImgNet and DL3DV-7K datasets, underscoring its capability in generating and editing complex 3D content without additional training. The framework's performance is strengthened by experimental results, where it outperforms existing methods in tasks entailing 3DGS generation, 3DGS editing, and camera pose estimation. The paper highlights quantifiable improvements over related works, showing significant gains in FID and CLIP scores, illustrating the efficacy of SplatFlow's unified approach.

Implications and Future Directions

The implications of this research are twofold. Practically, SplatFlow provides a robust platform for industries such as gaming, VR/AR, and robotics, where a streamlined pipeline for 3D content creation is highly valuable. Theoretically, the introduction of a unified model capable of handling both generation and editing could inspire new methodologies in 3D scene synthesis and editing. The paper's discussion on future developments hints at applications beyond scenic generation, opening avenues for nuanced control over 3D environments, potentially pushing towards more interactive and immersive content creation systems.

Conclusion

SplatFlow represents a significant advancement in the domain of text-to-3D scene generation and editing. By unifying these typically divergent tasks into a single framework, it proposes a compelling model that could redefine existing workflows in 3D content creation. While the paper successfully presents a novel method augmented by robust results, future research could focus on expanding this model's capabilities across broader datasets and enhancing real-time application potentials. This paper stands as a substantial contribution to the fields of computer vision and graphics, paving the way for further exploration and innovation in seamless 3D content manipulation.