Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

Humans have long been recorded in a variety of forms since antiquity. For example,
sculptures and paintings were the primary media for depicting human beings before the …

We present GANimator, a generative model that learns to synthesize novel motions from a
single, short motion sequence. GANimator generates motions that resemble the core …

In this work, we present MoConVQ, a novel unified framework for physics-based motion
control leveraging scalable discrete representations. Building upon vector quantized …

Non-verbal social human behavior forecasting has increasingly attracted the interest of the
research community in recent years. Its direct applications to human-robot interaction and …

Existing 3D human object interaction (HOI) datasets and models simply align global
descriptions with the long HOI sequence, while lacking a detailed understanding of …

Humans interact with objects all the time. Enabling a humanoid to learn human-object
interaction (HOI) is a key step for future smart animation and intelligent robotics systems …

Simulated humanoids are an appealing research domain due to their physical capabilities.
Nonetheless, they are also challenging to control, as a policy must drive an unstable …

In the fourth industrial revolution, the scale of execution for interactive applications increased
substantially. These interactive and animated applications are human-centric, and the …