Comments
cr125rider t1_iue124j wrote
Dear got that left side is a noisy mess. Did you get tracking from a 2002 Logitech web cam?
apachaves_ t1_iue1gq7 wrote
Looks cool!
Hataitai1977 t1_iue61ju wrote
I read the left hand side one as Erogenous Input. Seemed about right.
marauderingman t1_iuezhlz wrote
Touching, without U.
realGharren t1_iufh4fa wrote
They forgot to mention that the left guy drank too much coffee.
HenryDeTamble t1_iufwthy wrote
Can you point to us on this elephant where he touched you?
Overall-Run3216 t1_iuh4xrl wrote
Is there an analog trick to cheaping processing?
verified-cat t1_iuhdrbp wrote
*starts break dancing with own fingers
SpatialComputing OP t1_iud8iek wrote
>TOCH: SPATIO-TEMPORAL OBJECT-TO-HAND CORRESPONDENCE FOR MOTION REFINEMENT
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>We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection or missing contacts. Although correcting such errors requires reasoning about temporal aspects of interaction, most previous work focus on static grasps and contacts. The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction. The key component is a point-wise object-centric representation which encodes the hand position relative to the object. Leveraging this novel representation, we learn a latent manifold of plausible TOCH fields with a temporal denoising auto-encoder. Experiments demonstrate that TOCH outperforms state-of-the-art (SOTA) 3D hand-object interaction models, which are limited to static grasps and contacts. More importantly, our method produces smooth interactions even before and after contact. Using a single trained TOCH model, we quantitatively and qualitatively demonstrate its usefulness for 1) correcting erroneous reconstruction results from off-the-shelf RGB/RGB-D hand-object reconstruction methods, 2) de-noising, and 3) grasp transfer across objects.
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>Project | Paper | Code