The paper releases the ArtiAD benchmark with 15k+ articulated point clouds and shows that a pose-conditioned SDF baseline reaches 0.884/0.874 AUROC on seen/unseen joint configurations, outperforming rigid priors.
D-nerf: Neural radiance fields for dynamic scenes
10 Pith papers cite this work. Polarity classification is still indexing.
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NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
SAGE self-learns Gaussian expression deformations via joint surfel-SDF optimization and self-supervised consistency, enabling comparable avatar quality from single frames, monocular rotations, or one-shot inputs.
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.
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.
MatPhys is a feed-forward framework that predicts consistent part-level spring-mass parameters for deformable object simulation from monocular videos using semantic decomposition and a material embedding codebook.
Velox compresses dynamic point clouds into latent tokens that support geometry via 4D surface modeling and appearance via 3D Gaussians, showing strong results on video-to-4D generation, tracking, and image-to-4D cloth simulation.
Structure-guided dynamic 3DGS methods deliver superior reconstruction fidelity and compactness on D-NeRF while gaussian-centric methods provide higher rendering speeds at the cost of quality variability and storage.
Dual-representation framework pairs fixed-topology meshes for physics with Gaussian splatting for rendering, but two conversion strategies from varying-topology reconstructions cause 65-80% geometric degradation and underperform native fixed-topology methods.
citing papers explorer
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Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection
The paper releases the ArtiAD benchmark with 15k+ articulated point clouds and shows that a pose-conditioned SDF baseline reaches 0.884/0.874 AUROC on seen/unseen joint configurations, outperforming rigid priors.
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No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos
NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
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Self-Learning Expression Deformations for Data-Efficient Gaussian Avatars
SAGE self-learns Gaussian expression deformations via joint surfel-SDF optimization and self-supervised consistency, enabling comparable avatar quality from single frames, monocular rotations, or one-shot inputs.
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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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MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos
MatPhys is a feed-forward framework that predicts consistent part-level spring-mass parameters for deformable object simulation from monocular videos using semantic decomposition and a material embedding codebook.
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Velox: Learning Representations of 4D Geometry and Appearance
Velox compresses dynamic point clouds into latent tokens that support geometry via 4D surface modeling and appearance via 3D Gaussians, showing strong results on video-to-4D generation, tracking, and image-to-4D cloth simulation.
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Beyond Static Gaussians: An Empirical Investigation of Architectural Paradigms for Dynamic 3D Scene Reconstruction
Structure-guided dynamic 3DGS methods deliver superior reconstruction fidelity and compactness on D-NeRF while gaussian-centric methods provide higher rendering speeds at the cost of quality variability and storage.
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Real-Time Physics Simulation with Dynamic Mesh-Gaussian Reconstructions
Dual-representation framework pairs fixed-topology meshes for physics with Gaussian splatting for rendering, but two conversion strategies from varying-topology reconstructions cause 65-80% geometric degradation and underperform native fixed-topology methods.
- GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting