  Listening head generation aims to synthesize a non-verbal responsive listener
head by modeling the correlation between the speaker and the listener in
dynamic conversion.The applications of listener agent generation in virtual
interaction have promoted many works achieving the diverse and fine-grained
motion generation. However, they can only manipulate motions through simple
emotional labels, but cannot freely control the listener's motions. Since
listener agents should have human-like attributes (e.g. identity, personality)
which can be freely customized by users, this limits their realism. In this
paper, we propose a user-friendly framework called CustomListener to realize
the free-form text prior guided listener generation. To achieve
speaker-listener coordination, we design a Static to Dynamic Portrait module
(SDP), which interacts with speaker information to transform static text into
dynamic portrait token with completion rhythm and amplitude information. To
achieve coherence between segments, we design a Past Guided Generation Module
(PGG) to maintain the consistency of customized listener attributes through the
motion prior, and utilize a diffusion-based structure conditioned on the
portrait token and the motion prior to realize the controllable generation. To
train and evaluate our model, we have constructed two text-annotated listening
head datasets based on ViCo and RealTalk, which provide text-video paired
labels. Extensive experiments have verified the effectiveness of our model.
