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Found 9 papers, 6 papers with code
Self-Learning Transformations for Improving Gaze and Head Redirection
Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation.
A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
We propose a doubly stochastic simulator that integrates a stochastic generative neural network and a classical Monte Carlo Poisson simulator, to utilize both advantages.
Continuous Conditional Generative Adversarial Networks (cGAN) with Generator Regularization
To partially alleviate this difficulty, we propose a simple generator regularization term on the GAN generator loss in the form of Lipschitz penalty.
SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes
However, this is problematic since the backward warp field is pose dependent and thus requires large amounts of data to learn.
I M Avatar: Implicit Morphable Head Avatars from Videos
Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details.
PointAvatar: Deformable Point-based Head Avatars from Videos
The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment.
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
FLARE: Fast Learning of Animatable and Relightable Mesh Avatars
Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems.
A Unified Approach for Text- and Image-guided 4D Scene Generation
Large-scale diffusion generative models are greatly simplifying image, video and 3D asset creation from user-provided text prompts and images.
