Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models

• URL: http://arxiv.org/abs/2511.00503v1
• Date: Sat, 01 Nov 2025 11:16:25 GMT
• Title: Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models
• Authors: Panwang Pan, Chenguo Lin, Jingjing Zhao, Chenxin Li, Yuchen Lin, Haopeng Li, Honglei Yan, Kairun Wen, Yunlong Lin, Yixuan Yuan, Yadong Mu,
• Abstract summary: We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image.<n>Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion.
• Score: 79.06910348413861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image. Our approach unifies the generative priors of video diffusion models with geometry and motion constraints learned from large-scale 4D datasets. Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion, all in a single forward pass, without test-time optimization or post-hoc refinement. At the core of our framework lies a video latent transformer, which augments video diffusion models to jointly capture spatio-temporal dependencies and predict time-varying 3D Gaussian primitives. Training is guided by objectives on appearance fidelity, geometric accuracy, and motion consistency, enabling Diff4Splat to synthesize high-quality 4D scenes in 30 seconds. We demonstrate the effectiveness of Diff4Splatacross video generation, novel view synthesis, and geometry extraction, where it matches or surpasses optimization-based methods for dynamic scene synthesis while being significantly more efficient.

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