A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis

The field has made significant progress in synthesizing realistic human motion driven by various modalities. Yet, the need for different methods to animate various body parts according to different control signals limits the scalability of these techniques in practical scenarios. In this paper, we introduce a cohesive and scalable approach that consolidates multimodal (text, music, speech) and multi-part (hand, torso) human motion generation. Our methodology unfolds in several steps: We begin by quantizing the motions of diverse body parts into separate codebooks tailored to their respective domains. Next, we harness the robust capabilities of pre-trained models to transcode multimodal signals into a shared latent space. We then translate these signals into discrete motion tokens by iteratively predicting subsequent tokens to form a complete sequence. Finally, we reconstruct the continuous actual motion from this tokenized sequence. Our method frames the multimodal motion generation challenge as a token prediction task, drawing from specialized codebooks based on the modality of the control signal. This approach is inherently scalable, allowing for the easy integration of new modalities. Extensive experiments demonstrated the effectiveness of our design, emphasizing its potential for broad application.Comment: 19 pages, 18 figure

AvatarGPT: All-in-One Framework for Motion Understanding, Planning, Generation and Beyond

Large Language Models(LLMs) have shown remarkable emergent abilities in unifying almost all (if not every) NLP tasks. In the human motion-related realm, however, researchers still develop siloed models for each task. Inspired by InstuctGPT, and the generalist concept behind Gato, we introduce AvatarGPT, an All-in-One framework for motion understanding, planning, generations as well as other tasks such as motion in-between synthesis. AvatarGPT treats each task as one type of instruction fine-tuned on the shared LLM. All the tasks are seamlessly interconnected with language as the universal interface, constituting a closed-loop within the framework. To achieve this, human motion sequences are first encoded as discrete tokens, which serve as the extended vocabulary of LLM. Then, an unsupervised pipeline to generate natural language descriptions of human action sequences from in-the-wild videos is developed. Finally, all tasks are jointly trained. Extensive experiments show that AvatarGPT achieves SOTA on low-level tasks, and promising results on high-level tasks, demonstrating the effectiveness of our proposed All-in-One framework. Moreover, for the first time, AvatarGPT enables a principled approach by iterative traversal of the tasks within the closed-loop for unlimited long-motion synthesis.Comment: 22 pages, 21 figure

Searching for Dark Matter Signals in the Left-Right Symmetric Gauge Model with CP Symmetry

We investigate singlet scalar dark matter (DM) candidate in a left-right symmetric gauge model with two Higgs bidoublets (2HBDM) in which the stabilization of the DM particle is induced by the discrete symmetries P and CP. According to the observed DM abundance, we predict the DM direct and indirect detection cross sections for the DM mass range from 10 GeV to 500 GeV. We show that the DM indirect detection cross section is not sensitive to the light Higgs mixing and Yukawa couplings except the resonance regions. The predicted spin-independent DM-nucleon elastic scattering cross section is found to be significantly dependent on the above two factors. Our results show that the future DM direct search experiments can cover the most parts of the allowed parameter space. The PAMELA antiproton data can only exclude two very narrow regions in the 2HBDM. It is very difficult to detect the DM direct or indirect signals in the resonance regions due to the Breit-Wigner resonance effect.Comment: 24 pages, 8 figures. minor changes and a reference added, published in Phys. Rev.