MMoFusion: Multi-modal Co-Speech Motion Generation with Diffusion Model

Centrala begrepp

The authors propose MMoFusion, a framework for generating diverse and realistic co-speech motion using a diffusion model. They employ a Progressive Fusion Strategy to integrate multi-modal information efficiently.

Sammanfattning

The MMoFusion framework aims to generate realistic avatars by synthesizing body movements accompanying speech. It utilizes a diffusion model to ensure authenticity and diversity in generated motion. The proposed Progressive Fusion Strategy enhances the interaction of inter-modal and intra-modal information, resulting in vivid, diverse, and style-controllable motion. Specific and shared feature encoding is used to refine multi-modal information, while a geometric loss enforces joint velocity and acceleration coherence. Long sequence sampling allows for consistent motion generation of arbitrary length.

Statistik

Fig. 1: Our MMoFusion framework generates realistic, coherent, and diverse motions conditioned on speech, editable identities, and emotions.
Abstract: Due to the intricate correspondence between speech and motion, generating realistic and diverse motion is challenging.
ArXiv:2403.02905v1 [cs.MM] 5 Mar 2024

Citat

"Our framework generates vivid, diverse, and style-controllable motion of arbitrary length through inputting speech."
"Extensive experiments demonstrate that our method outperforms current co-speech motion generation methods."

Viktiga insikter från

MMoFusion

by Sen Wang,Jia... på arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02905.pdf

Djupare frågor

How can the ethical concerns related to co-speech motion generation be addressed responsibly?

Co-speech motion generation raises several ethical concerns that need to be addressed responsibly. One key concern is privacy, as generating realistic avatars from speech data could potentially infringe on individuals' rights if their likeness is used without consent. To address this, strict guidelines and regulations should be put in place regarding the collection, storage, and usage of speech data for motion generation. Transparency about how the data is being used and obtaining explicit consent from individuals before using their data are crucial steps.
Another important ethical consideration is the potential for misuse or manipulation of generated motions for deceptive purposes such as deepfakes or misinformation. Responsible development practices involve implementing safeguards against malicious use cases through robust authentication mechanisms and ensuring that generated content is clearly marked as synthetic.
Furthermore, addressing societal biases in training data and algorithms is essential to prevent perpetuating stereotypes or discrimination in generated motions. Diversity and inclusivity should be prioritized in dataset collection and model training to ensure fair representation across different demographics.
Overall, a multidisciplinary approach involving experts in ethics, law, technology, psychology, and other relevant fields should collaborate to develop comprehensive guidelines for responsible co-speech motion generation practices.

What are the potential implications of using diffusion models for generating human-like motions?

Using diffusion models for generating human-like motions offers several implications both technically and creatively. From a technical standpoint, diffusion models excel at capturing complex distributions without making assumptions about target distributions. This allows for more flexibility in modeling intricate relationships between speech cues and corresponding body movements during motion synthesis.
Creatively, diffusion models enable the generation of diverse and realistic human-like motions by leveraging multi-modal information such as text transcripts, audio features, identity labels, and emotional cues. The progressive fusion strategy employed with diffusion models enhances style control over generated motions while maintaining authenticity.
Diffusion models also facilitate smoother transitions between frames by incorporating geometric loss functions that enforce joint velocity and acceleration coherence among frames. This results in more natural-looking animations with improved physical realism compared to traditional methods.
Additionally,
the long sequence sampling technique enables continuous
motion synthesis regardless of input length,
enhancing scalability
and adaptability across various applications requiring dynamic
motion sequences.

How might the integration of additional modalities like audio impact the realism of generated motions?

The integration of additional modalities like audio can significantly impact the realism of generated motions by enriching the contextual information available during synthesis.
Audio features provide valuable insights into intonation,
emotional expression,
and pacing within spoken language.
By incorporating these auditory cues into co-speech motion generation frameworks,
the resulting animations can better reflect nuances present
in natural conversations.
For example,
audio signals indicating excitement may lead to more energetic gestures,
while variations in pitch could influence movement speed or intensity.
This multi-modal approach not only enhances realism but also improves synchronization between speech content
and corresponding bodily expressions.
Moreover,
integrating audio features can enhance diversity
by introducing new dimensions
for style modulation based on acoustic characteristics such as tone quality
or prosodic elements.
Overall,
the integration of audio modalities contributes
to a more immersive user experience
by creating lifelike avatars capable
of accurately mirroring verbal communication patterns alongside visual representations