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
6 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.
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.
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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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.
- GSDeformer: Direct, Real-time and Extensible Cage-based Deformation for 3D Gaussian Splatting