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.