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Found 6 papers, 1 papers with code
MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters.
Neural Mesh-Based Graphics
We also perform competitively with respect to the state-of-the-art method SVS, which has been trained on the full dataset (DTU and Tanks and Temples) and then scene finetuned, in spite of their deeper neural renderer.
Learning Generalizable Light Field Networks from Few Images
We explore a new strategy for few-shot novel view synthesis based on a neural light field representation.
FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks
In this paper, we present FaceTuneGAN, a new 3D face model representation decomposing and encoding separately facial identity and facial expression.
Monocular Human Shape and Pose with Dense Mesh-borne Local Image Features
We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features.
Neural Human Deformation Transfer
We consider the problem of human deformation transfer, where the goal is to retarget poses between different characters.
