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Found 9 papers, 2 papers with code
Boosting Continuous Emotion Recognition with Self-Pretraining using Masked Autoencoders, Temporal Convolutional Networks, and Transformers
Human emotion recognition holds a pivotal role in facilitating seamless human-computer interaction.
ContactGen: Generative Contact Modeling for Grasp Generation
This paper presents a novel object-centric contact representation ContactGen for hand-object interaction.
CTR is not Enough: a Novel Reinforcement Learning based Ranking Approach for Optimizing Session Clicks
However, we found that the traditional ranking method for optimizing Click-Through rate(CTR) cannot address our ranking scenario well, since it completely ignores user leaving, and CTR is the optimization goal for the one-step recommendation.
Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds
We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints.
CASA: Category-agnostic Skeletal Animal Reconstruction
Recovering the skeletal shape of an animal from a monocular video is a longstanding challenge.
Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos
To tackle this task, we first provide an automatic way to collect trajectory and hotspots labels on large-scale data.
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time
Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly.
Ranked #7 on
hand-object pose
on HO-3D
Hand-Object Contact Consistency Reasoning for Human Grasps Generation
Based on the hand-object contact consistency, we design novel objectives in training the human grasp generation model and also a new self-supervised task which allows the grasp generation network to be adjusted even during test time.
