SS4D: Native 4D Generative Model via Structured Spacetime Latents
We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a generator directly on 4D data, achieving high fidelity, temporal coherence, and structural consistency. At the core of our method is a compressed set of structured spacetime latents. Specifically, (1) To address the scarcity of 4D training data, we build on a pre-trained single-image-to-3D model, preserving strong spatial consistency. (2) Temporal consistency is enforced by introducing dedicated temporal layers that reason across frames. (3) To support efficient training and inference over long video sequences, we compress the latent sequence along the temporal axis using factorized 4D convolutions and temporal downsampling blocks. In addition, we employ a carefully designed training strategy to enhance robustness against occlusion
6942361f5d5b2dc1052748e0
Zhibing Li
6942361f5d5b2dc1052748e1
Mengchen Zhang
6942361f5d5b2dc1052748e2
Tong Wu
6942361f5d5b2dc1052748e3
Jing Tan
claimed_verified
2026-01-06T10:04:12.835Z
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/avatars/4baf27263841471cbd5f629a8b99424d.svg
Jing Tan
jingtan
user
jingtan
6942361f5d5b2dc1052748e4
Jiaqi Wang
claimed_verified
2026-02-17T15:49:24.837Z
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/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
Jiaqi Wang
myownskyW7
user
myownskyW7
6942361f5d5b2dc1052748e5
Dahua Lin
SS4D: Native 4D Generative Model via Structured Spacetime Latents
We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a generator directly on 4D data, achieving high fidelity, temporal coherence, and structural consistency. At the core of our method is a compressed set of structured spacetime latents. Specifically, (1) To address the scarcity of 4D training data, we build on a pre-trained single-image-to-3D model, preserving strong spatial consistency. (2) Temporal consistency is enforced by introducing dedicated temporal layers that reason across frames. (3) To support efficient training and inference over long video sequences, we compress the latent sequence along the temporal axis using factorized 4D convolutions and temporal downsampling blocks. In addition, we employ a carefully designed training strategy to enhance robustness against occlusion
SS4D synthesizes dynamic 3D objects from monocular video using a native 4D generative model with structured spacetime latents, ensuring high fidelity, temporal coherence, and structural consistency.
4D generative model dynamic 3D objects monocular video 4D representations spatial consistency temporal layers factorized 4D convolutions temporal downsampling blocks