GraG reconstructs dynamic 3D hand-object interactions from monocular video 6.4x faster than prior work by using compact Sum-of-Gaussians tracking initialized from large models and refined with 2D losses.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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Proprioception and multi-contact touch, fused with a physics-guided conditional diffusion model over a Structure-VAE SDF latent space, improve metric amodal object reconstruction under severe hand occlusion versus vision-only baselines.
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.
citing papers explorer
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Grasp in Gaussians: Fast Monocular Reconstruction of Dynamic Hand-Object Interactions
GraG reconstructs dynamic 3D hand-object interactions from monocular video 6.4x faster than prior work by using compact Sum-of-Gaussians tracking initialized from large models and refined with 2D losses.
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Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch
Proprioception and multi-contact touch, fused with a physics-guided conditional diffusion model over a Structure-VAE SDF latent space, improve metric amodal object reconstruction under severe hand occlusion versus vision-only baselines.
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Pose-Aware Diffusion for 3D Generation
PAD synthesizes 3D geometry in observation space via depth unprojection as anchor to eliminate pose ambiguity in image-to-3D generation.