Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916066834251776 |
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| author | Kim, Mungyeom Jeon, Minkyeong An, Honggyu Jung, Jaewoo Ko, Hyuna Han, Jisang Yu, Hyeonseo Shin, Donghwan Hong, Sunghwan Narihira, Takuya Fukuda, Kazumi Mitsufuji, Yuki Kim, Seungryong |
| author_facet | Kim, Mungyeom Jeon, Minkyeong An, Honggyu Jung, Jaewoo Ko, Hyuna Han, Jisang Yu, Hyeonseo Shin, Donghwan Hong, Sunghwan Narihira, Takuya Fukuda, Kazumi Mitsufuji, Yuki Kim, Seungryong |
| contents | Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens. Each token aggregates corresponding features across the full temporal context and decodes a 3D Gaussian whose position is modulated by the target timestamp, enabling globally coherent motion modeling without per-scene optimization. To capture fine-grained details, we further introduce a video diffusion model-based rendering enhancement module. Since our framework effectively aggregates features into Gaussians, we extend this capability to feature lifting, producing a 4D feature field that supports point tracking and dynamic scene understanding. C4G achieves strong novel-view synthesis performance using significantly fewer Gaussians and without requiring camera poses, while exhibiting stronger motion modeling and robustness to large temporal gaps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31595 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction Kim, Mungyeom Jeon, Minkyeong An, Honggyu Jung, Jaewoo Ko, Hyuna Han, Jisang Yu, Hyeonseo Shin, Donghwan Hong, Sunghwan Narihira, Takuya Fukuda, Kazumi Mitsufuji, Yuki Kim, Seungryong Computer Vision and Pattern Recognition Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens. Each token aggregates corresponding features across the full temporal context and decodes a 3D Gaussian whose position is modulated by the target timestamp, enabling globally coherent motion modeling without per-scene optimization. To capture fine-grained details, we further introduce a video diffusion model-based rendering enhancement module. Since our framework effectively aggregates features into Gaussians, we extend this capability to feature lifting, producing a 4D feature field that supports point tracking and dynamic scene understanding. C4G achieves strong novel-view synthesis performance using significantly fewer Gaussians and without requiring camera poses, while exhibiting stronger motion modeling and robustness to large temporal gaps. |
| title | Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.31595 |