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Found 8 papers, 1 papers with code
Chinese Synesthesia Detection: New Dataset and Models
In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word.
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains.
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images.
Learning Controllable Adaptive Simulation for Multi-resolution Physics
In this work, we introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions.
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
Given a sparse set of measurements, we are interested in recovering the initial condition or parameters of the PDE.
