OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from monocular video via a four-stage training-free pipeline and introduces a new benchmark for structured Video-to-4D evaluation.
Mitra, and Tom Monnier
7 Pith papers cite this work. Polarity classification is still indexing.
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A self-supervised framework learns implicit 3D physics by lifting V-JEPA features into voxels and performing volumetric feature advection conditioned on actions.
T2Mo generates controllable dynamic 3D shapes by conditioning on both text semantics and 3D trajectories with a shape-grounded embedding for arbitrary inputs.
MORPHOS introduces an autoregressive 4D generation method with Temporal Structured Latents (T-SLAT) that produces dynamic 3D assets from videos while handling topological changes and long sequences.
A training-free Spatio-Temporal Attention Chain framework accelerates 4D mesh generation 13x, improves quality, scales to 16x longer videos, and supports downstream tracking and camera estimation.
Skelebones compresses 4D Gaussian shapes into compact, controllable bones and skeletons, delivering 17.3% PSNR gains over LBS and 21.7% over BoB for unseen poses while preserving reconstruction quality.
SkelMo introduces a category-agnostic diffusion framework for skeletal motion generation from 2D videos, trained on a new dataset of ~20,000 rigged 3D animations with a structural-semantic injection mechanism.
citing papers explorer
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One Video, One World: Turning Monocular Video into Physical 4D Scenes
OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from monocular video via a four-stage training-free pipeline and introduces a new benchmark for structured Video-to-4D evaluation.
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Neural Voxel Dynamics: Learning Implicit 3D Physics via Volumetric Feature Advection
A self-supervised framework learns implicit 3D physics by lifting V-JEPA features into voxels and performing volumetric feature advection conditioned on actions.
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Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text
T2Mo generates controllable dynamic 3D shapes by conditioning on both text semantics and 3D trajectories with a shape-grounded embedding for arbitrary inputs.
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MORPHOS: Autoregressive 4D Generation with Temporal Structured Latents
MORPHOS introduces an autoregressive 4D generation method with Temporal Structured Latents (T-SLAT) that produces dynamic 3D assets from videos while handling topological changes and long sequences.
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Fast 4D Mesh Generation by Spatio-Temporal Attention Chains
A training-free Spatio-Temporal Attention Chain framework accelerates 4D mesh generation 13x, improves quality, scales to 16x longer videos, and supports downstream tracking and camera estimation.
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GaussiAnimate: Reconstruct and Rig Animatable Categories with Level of Dynamics
Skelebones compresses 4D Gaussian shapes into compact, controllable bones and skeletons, delivering 17.3% PSNR gains over LBS and 21.7% over BoB for unseen poses while preserving reconstruction quality.
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SkelMo: Universal Skeletal Motion Generation for 3D Rigged Shapes
SkelMo introduces a category-agnostic diffusion framework for skeletal motion generation from 2D videos, trained on a new dataset of ~20,000 rigged 3D animations with a structural-semantic injection mechanism.