All about ZeroEggs publication ( https://arxiv.org/abs/2209.07556 ) - GitHub - ubisoft/ubisoft-laforge-ZeroEGGS: All about ZeroEggs publication ( https://arxiv.org/abs/2209.07556 )

We present ZeroEGGS, a neural network framework for speech-driven gesture generation with zero-shot style control by example. This means style can be controlled via only a short example motion clip, even for motion styles unseen during training. Our model uses a Variational framework to learn a style embedding, making it easy to modify style through latent space manipulation or blending and scaling of style embeddings. The probabilistic nature of our framework further enables the generation of a variety of outputs given the same input, addressing the stochastic nature of gesture motion. In a series of experiments, we first demonstrate the flexibility and generalizability of our model to new speakers and styles. In a user study, we then show that our model outperforms previous state-of-the-art techniques in naturalness of motion, appropriateness for speech, and style portrayal. Finally, we release a high-quality dataset of full-body gesture motion including fingers, with speech, spanning across 19 different styles.

Human-like articulated neural avatars have several uses in telepresence, animation, and visual content production. These neural avatars must be simple to create, simple to animate in new stances and views, capable of rendering in photorealistic picture quality, and simple to relight in novel situations if they are to be widely adopted. Existing techniques frequently use monocular films to teach these neural avatars. While the method permits movement and photorealistic image quality, the synthesized images are constantly constrained by the training video's lighting conditions. Other studies specifically address the relighting of human avatars. However, they do not provide the user control

A Brief Comparison Between Human Perception and Multimodal Language Models

# Paths toward single-slot finality _Special thanks to Justin Drake, Dankrad Feist, Alex Obadia, Ha