MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting
Chen Tessler, Yunrong Guo, Ofir Nabati, Gal Chechik, Xue Bin Peng

TL;DR
MaskedMimic introduces a unified physics-based character control method that uses masked motion inpainting to synthesize diverse animations from partial descriptions, enabling versatile and adaptive virtual characters.
Contribution
It formulates character control as a general motion inpainting problem, allowing a single model to handle multiple control modalities without extensive reward engineering.
Findings
Supports diverse control modalities including keyframes, text, and scene info.
Enables seamless transitions between different tasks.
Creates versatile characters capable of dynamic adaptation.
Abstract
Crafting a single, versatile physics-based controller that can breathe life into interactive characters across a wide spectrum of scenarios represents an exciting frontier in character animation. An ideal controller should support diverse control modalities, such as sparse target keyframes, text instructions, and scene information. While previous works have proposed physically simulated, scene-aware control models, these systems have predominantly focused on developing controllers that each specializes in a narrow set of tasks and control modalities. This work presents MaskedMimic, a novel approach that formulates physics-based character control as a general motion inpainting problem. Our key insight is to train a single unified model to synthesize motions from partial (masked) motion descriptions, such as masked keyframes, objects, text descriptions, or any combination thereof. This is…
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Taxonomy
TopicsHuman Motion and Animation · Handwritten Text Recognition Techniques · Simulation and Modeling Applications
MethodsSparse Evolutionary Training · Inpainting
