TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through
  Task Tokenization
Authors: name: Liang Pan, name: Zeshi Yang, name: Zhiyang Dou, name: Wenjia Wang, name: Buzhen Huang, name: Bo Dai, name: Taku Komura, name: Jingbo Wang

Synthesizing diverse and physically plausible Human-Scene Interactions (HSI)
is pivotal for both computer animation and embodied AI. Despite encouraging
progress, current methods mainly focus on developing separate controllers, each
specialized for a specific interaction task. This significantly hinders the
ability to tackle a wide variety of challenging HSI tasks that require the
integration of multiple skills, e.g., sitting down while carrying an object. To
address this issue, we present TokenHSI, a single, unified transformer-based
policy capable of multi-skill unification and flexible adaptation. The key
insight is to model the humanoid proprioception as a separate shared token and
combine it with distinct task tokens via a masking mechanism. Such a unified
policy enables effective knowledge sharing across skills, thereby facilitating
the multi-task training. Moreover, our policy architecture supports variable
length inputs, enabling flexible adaptation of learned skills to new scenarios.
By training additional task tokenizers, we can not only modify the geometries
of interaction targets but also coordinate multiple skills to address complex
tasks. The experiments demonstrate that our approach can significantly improve
versatility, adaptability, and extensibility in various HSI tasks. Website:
https://liangpan99.github.io/TokenHSI/