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Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
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Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
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Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering. Nonetheless, cutting-edge dynamic neural rendering methods rely heavily on these implicit representations, which frequently struggle to capture the intricate details of objects in the scene. Furthermore, implicit methods have difficulty achieving real-time rendering in general dynamic scenes, limiting their use in a variety of tasks. To address the issues, we propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead, which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world datasets. Through a differential Gaussian rasterizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed, making it well-suited for tasks such as novel-view synthesis, time interpolation, and real-time rendering.
Forward citations
Cited by 18 Pith papers
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R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction
R5DGS augments physics-driven 4D Gaussian splatting with identity encodings and centroid-only rigid-body dynamics to enable semantic open-vocabulary retrieval and 11 FPS faster extrapolation.
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RiGS: Rigid-aware 4D Gaussian Splatting from a Single Monocular Video
RiGS decomposes scenes into static, rigid, and transient 4D Gaussians with an object-wise dynamic mask and scene flow guidance to model multi-scale motions and achieve SOTA novel view synthesis.
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WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations
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GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation
GaussianDWM uses 3D Gaussians with embedded linguistic features, language-guided sampling, and dual-condition generation for unified scene understanding and multi-modal output in driving world models.
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VDAWorld: World Modelling via VLM-Directed Abstraction and Simulation
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Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models
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BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
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WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos
WebSpline uses learnable cubic Hermite splines guided by a Structural Proxy Graph to deliver state-of-the-art quality dynamic 3D Gaussian rendering from monocular videos at over 10x the speed of prior methods on iPhon...
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