Daily Papers

The ability to track general whole-body motions in the real world is a useful way to build general-purpose humanoid robots. However, achieving this can be challenging due to the temporal and kinematic diversity of the motions, the policy's capability, and the difficulty of coordination of the upper and lower bodies. To address these issues, we propose GMT, a general and scalable motion-tracking framework that trains a single unified policy to enable humanoid robots to track diverse motions in the real world. GMT is built upon two core components: an Adaptive Sampling strategy and a Motion Mixture-of-Experts (MoE) architecture. The Adaptive Sampling automatically balances easy and difficult motions during training. The MoE ensures better specialization of different regions of the motion manifold. We show through extensive experiments in both simulation and the real world the effectiveness of GMT, achieving state-of-the-art performance across a broad spectrum of motions using a unified general policy. Videos and additional information can be found at https://gmt-humanoid.github.io.

Synthesizing high-fidelity and emotion-controllable talking video portraits, with audio-lip sync, vivid expression, realistic head pose, and eye blink, is an important and challenging task in recent years. Most of the existing methods suffer in achieving personalized precise emotion control or continuously interpolating between different emotions and generating diverse motion. To address these problems, we present GMTalker, a Gaussian mixture based emotional talking portraits generation framework. Specifically, we propose a Gaussian Mixture based Expression Generator (GMEG) which can construct a continuous and multi-modal latent space, achieving more flexible emotion manipulation. Furthermore, we introduce a normalizing flow based motion generator pretrained on the dataset with a wide-range motion to generate diverse motions. Finally, we propose a personalized emotion-guided head generator with an Emotion Mapping Network (EMN) which can synthesize high-fidelity and faithful emotional video portraits. Both quantitative and qualitative experiments demonstrate our method outperforms previous methods in image quality, photo-realism, emotion accuracy and motion diversity.