REVNET is a rotation-equivariant point cloud completion model using Vector Neuron anchors and transformers that outperforms prior methods on synthetic MVP data and matches non-equivariant baselines on real KITTI data without input alignment.
In: NeurIPS (2017)
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HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.
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REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer
REVNET is a rotation-equivariant point cloud completion model using Vector Neuron anchors and transformers that outperforms prior methods on synthetic MVP data and matches non-equivariant baselines on real KITTI data without input alignment.
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HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching
HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.