{"ID":2847235,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00503","arxiv_id":"2511.00503","title":"Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.00503","url_pdf":"https://arxiv.org/pdf/2511.00503v2","authors":"[\"Panwang Pan\",\"Chenguo Lin\",\"Jingjing Zhao\",\"Chenxin Li\",\"Yuchen Lin\",\"Haopeng Li\",\"Honglei Yan\",\"Kairun Wen\",\"Yunlong Lin\",\"Yixuan Yuan\",\"Yadong Mu\"]","published":"2025-11-01T11:16:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}