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Found 9 papers, 4 papers with code
GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images.
Secure On-Device Video OOD Detection Without Backpropagation
While deploying personalized OOD detection directly on edge devices is desirable, it remains challenging due to large model sizes and the computational infeasibility of on-device training.
Treble Counterfactual VLMs: A Causal Approach to Hallucination
To address this, we construct a causal graph for VLMs and employ counterfactual analysis to estimate the Natural Direct Effect (NDE) of vision, text, and their cross-modal interaction on the output.
PuYun: Medium-Range Global Weather Forecasting Using Large Kernel Attention Convolutional Networks
This approach enhances the accuracy of 10-day weather forecasting.
Domain-wise Invariant Learning for Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) involves the detection of objects and the prediction of their corresponding relationships (predicates).
EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors
We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera.
TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors
For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique.
Monocular Real-time Full Body Capture with Inter-part Correlations
We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image.
Ranked #29 on
3D Hand Pose Estimation
on FreiHAND
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy.
