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

Panwang Pan; Chenguo Lin; Jingjing Zhao; Chenxin Li; Yuchen Lin; Haopeng Li; Honglei Yan; Kairun Wen; Yunlong Lin; Yixuan Yuan; Yadong Mu

arXiv · 2511.00503

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

Panwang Pan·Chenguo Lin·Jingjing Zhao·Chenxin Li·Yuchen Lin·Haopeng Li·Honglei Yan·Kairun Wen·Yunlong Lin·Yixuan Yuan·Yadong Mu

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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 Diff4Splat across 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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BibTeX RIS

Panwang Pan, Chenguo Lin, Jingjing Zhao, Chenxin Li, Yuchen Lin, Haopeng Li, Honglei Yan, Kairun Wen, Yunlong Lin, Yixuan Yuan, Yadong Mu. 2025-11-01. Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models. https://arxiv.org/abs/2511.00503

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