Instant4D: 4D Gaussian Splatting in Minutes
Dynamic view synthesis has seen significant advances, yet reconstructing
scenes from uncalibrated, casual video remains challenging due to slow
optimization and complex parameter estimation. In this work, we present
Instant4D, a monocular reconstruction system that leverages native 4D
representation to efficiently process casual video sequences within minutes,
without calibrated cameras or depth sensors. Our method begins with geometric
recovery through deep visual SLAM, followed by grid pruning to optimize scene
representation. Our design significantly reduces redundancy while maintaining
geometric integrity, cutting model size to under 10% of its original footprint.
To handle temporal dynamics efficiently, we introduce a streamlined 4D Gaussian
representation, achieving a 30x speed-up and reducing training time to within
two minutes, while maintaining competitive performance across several
benchmarks. Our method reconstruct a single video within 10 minutes on the
Dycheck dataset or for a typical 200-frame video. We further apply our model to
in-the-wild videos, showcasing its generalizability. Our project website is
published at https://instant4d.github.io/.
68ec5ec4cd07fb414898c939
Zhanpeng Luo
68ec5ec4cd07fb414898c93a
Haoxi Ran
68ec5ec4cd07fb414898c93b
Li Lu
Instant4D: 4D Gaussian Splatting in Minutes
Dynamic view synthesis has seen significant advances, yet reconstructing
scenes from uncalibrated, casual video remains challenging due to slow
optimization and complex parameter estimation. In this work, we present
Instant4D, a monocular reconstruction system that leverages native 4D
representation to efficiently process casual video sequences within minutes,
without calibrated cameras or depth sensors. Our method begins with geometric
recovery through deep visual SLAM, followed by grid pruning to optimize scene
representation. Our design significantly reduces redundancy while maintaining
geometric integrity, cutting model size to under 10% of its original footprint.
To handle temporal dynamics efficiently, we introduce a streamlined 4D Gaussian
representation, achieving a 30x speed-up and reducing training time to within
two minutes, while maintaining competitive performance across several
benchmarks. Our method reconstruct a single video within 10 minutes on the
Dycheck dataset or for a typical 200-frame video. We further apply our model to
in-the-wild videos, showcasing its generalizability. Our project website is
published at https://instant4d.github.io/.
Instant4D uses deep visual SLAM and a 4D Gaussian representation to efficiently reconstruct scenes from uncalibrated video sequences in minutes.
deep visual SLAM 4D representation grid pruning 4D Gaussian representation