  Transforming neutral, characterless input motions to embody the distinct
style of a notable character in real time is highly compelling for character
animation. This paper introduces MOCHA, a novel online motion characterization
framework that transfers both motion styles and body proportions from a target
character to an input source motion. MOCHA begins by encoding the input motion
into a motion feature that structures the body part topology and captures
motion dependencies for effective characterization. Central to our framework is
the Neural Context Matcher, which generates a motion feature for the target
character with the most similar context to the input motion feature. The
conditioned autoregressive model of the Neural Context Matcher can produce
temporally coherent character features in each time frame. To generate the
final characterized pose, our Characterizer network incorporates the
characteristic aspects of the target motion feature into the input motion
feature while preserving its context. This is achieved through a transformer
model that introduces the adaptive instance normalization and context
mapping-based cross-attention, effectively injecting the character feature into
the source feature. We validate the performance of our framework through
comparisons with prior work and an ablation study. Our framework can easily
accommodate various applications, including characterization with only sparse
input and real-time characterization. Additionally, we contribute a
high-quality motion dataset comprising six different characters performing a
range of motions, which can serve as a valuable resource for future research.
