CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation

Core Concepts

The author proposes CustomListener, a user-friendly framework for generating listener head motions guided by text priors. The approach involves dynamic portrait tokens and past-guided motion generation to achieve controllable and interactive responses.

Abstract

CustomListener introduces a framework for generating listener head motions based on text priors. It addresses the limitations of existing methods by allowing users to customize listener attributes and achieve realistic, controllable responses. The SDP module transforms static portraits into dynamic ones, while the PGG module ensures coherence between segments. Extensive experiments confirm the effectiveness of CustomListener in achieving state-of-the-art performance in listener motion generation.

Stats

ViCo dataset contains three parts: train set Dtrain, test set Dtest, out-of-domain set Dood.
ViCo dataset annotations include emotion labels, activated AUs, and head movements detected by Hopenet.
RealTalk dataset is used as a database for retrieving listener videos.
45-dim acoustic features extracted from audios include MFCC, MFCC-Delta, energy, Zero Crossing Rate, and loudness.
Resolution of facial videos is 256x256 with an FPS of 30.

Quotes

"The applications of listener agent generation in virtual interaction have promoted many works achieving diverse and fine-grained motion generation."
"Users can pre-customize detailed attributes of the listener agent."
"Our proposed CustomListener achieves lowest FD on both Dtest and Dood subsets in ViCo."

Key Insights Distilled From

CustomListener

by Xi Liu,Ying ... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00274.pdf

Deeper Inquiries

How can CustomListener be adapted to generate listening body movements?

CustomListener can be adapted to generate listening body movements by incorporating additional modules or components that focus on capturing and synthesizing full-body gestures and postures. This adaptation would involve extending the existing framework to include mechanisms for understanding and generating non-verbal cues beyond facial expressions, such as hand gestures, body orientation, and overall body language. By integrating pose estimation algorithms, motion capture data, or even 3D skeletal models into the system, CustomListener could analyze text prompts in conjunction with speaker information to produce synchronized and naturalistic full-body responses from the listener.

What are the implications of using large language models in text-guided motion synthesis?

The use of large language models in text-guided motion synthesis offers several significant implications:

Improved Semantic Understanding: Large language models enhance the system's ability to comprehend complex textual descriptions provided as input for guiding motion synthesis. These models excel at capturing nuanced meanings, context dependencies, and subtle emotional cues embedded within the text.

Enhanced Personalization: Leveraging large language models allows for more personalized and customized responses based on user-defined attributes or preferences. The model can adapt its output motions according to specific traits like identity, personality type, relationship dynamics with speakers, or emotional states specified in the input text.

Increased Realism: By utilizing sophisticated language processing capabilities, text-guided motion synthesis powered by large language models can create more realistic listener responses that align closely with human-like behaviors and reactions during interactions.

Fine-Grained Control: Large language models enable fine-grained control over generated motions by interpreting detailed textual instructions accurately. This level of control facilitates precise adjustments in response timing, rhythm variations, emotion expression levels, and other subtleties essential for creating authentic non-verbal communication gestures.

Scalability & Generalization: The scalability of large language models allows them to handle diverse inputs across various contexts effectively while maintaining high performance standards in generating coherent and contextually relevant motion sequences.

How does CustomListener address the limitations of existing methods in generating realistic listener responses?

CustomListener addresses key limitations present in existing methods for generating realistic listener responses through several innovative features:

User-Friendly Framework: CustomListener introduces a user-friendly framework that empowers users to customize detailed attributes of listener agents before generating their motions based on given textual prompts.

Dynamic Portrait Generation (SDP Module): The SDP module transforms static portrait tokens into dynamic ones through audio-text responsive interaction processes that consider completion time rhythms influenced by speaker semantics.

Past-Guided Motion Generation (PGG Module): The PGG module ensures coherence between segments by maintaining consistency in customized behavioral habits across different video clips while facilitating smooth transitions between distinct segments.

4 .Controllable Generation: Through a diffusion-based structure conditioned on dynamic portrait tokens and past-motion guidance signals from adjacent segments ,Custom Listener enables controllable generation where users have flexibility over how they want their listeners' emotions expressed
5 .Realism Enhancement: By combining speaker-listener coordination mechanisms with coherent long-term generation strategies ,Custom Listener enhances realism by ensuring synchronous non-verbal feedback aligned with speaker actions , tones ,and semantic content
Overall,Customer Listener's comprehensive approach tackles issues relatedto limited expressiveness,dynamic responsiveness,and coherence seenin previous methods,resultingin an advancedframeworkfor producing highlyrealisticlistenerresponsesbasedonuser-specifiedattributesandtextualprompts