Autonomous Character-Scene Interaction Synthesis from Text Instruction
Authors: name: Nan Jiang, name: Zimo He, name: Zi Wang, name: Hongjie Li, name: Yixin Chen, name: Siyuan Huang, name: Yixin Zhu

Synthesizing human motions in 3D environments, particularly those with
complex activities such as locomotion, hand-reaching, and human-object
interaction, presents substantial demands for user-defined waypoints and stage
transitions. These requirements pose challenges for current models, leading to
a notable gap in automating the animation of characters from simple human
inputs. This paper addresses this challenge by introducing a comprehensive
framework for synthesizing multi-stage scene-aware interaction motions directly
from a single text instruction and goal location. Our approach employs an
auto-regressive diffusion model to synthesize the next motion segment, along
with an autonomous scheduler predicting the transition for each action stage.
To ensure that the synthesized motions are seamlessly integrated within the
environment, we propose a scene representation that considers the local
perception both at the start and the goal location. We further enhance the
coherence of the generated motion by integrating frame embeddings with language
input. Additionally, to support model training, we present a comprehensive
motion-captured dataset comprising 16 hours of motion sequences in 120 indoor
scenes covering 40 types of motions, each annotated with precise language
descriptions. Experimental results demonstrate the efficacy of our method in
generating high-quality, multi-stage motions closely aligned with environmental
and textual conditions.