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.

翻译：我们提出了Diff4Splat，一种从单张图像合成可控且显式4D场景的前馈方法。该方法将视频扩散模型的生成先验与从大规模4D数据集中学习的几何与运动约束相统一。给定单张输入图像、相机轨迹及可选文本提示，Diff4Splat可直接预测可变形3D高斯场，该场编码了表观、几何与运动信息，且全部通过单次前向传播完成，无需测试时优化或事后精调。本框架的核心是一个视频潜在变换器，它增强视频扩散模型以联合捕获时空依赖性并预测时变3D高斯基元。训练过程由表观保真度、几何精度及运动一致性目标引导，使得Diff4Splat能够在30秒内合成高质量4D场景。我们在视频生成、新视角合成及几何提取任务中展示了Diff4Splat的有效性，其在动态场景合成方面达到或超越基于优化的方法，同时显著提升效率。