T3M: Text Guided 3D Human Motion Synthesis from Speech

🌈 Abstract

The paper proposes a novel text-guided 3D human motion synthesis method called T3M, which can generate realistic and expressive holistic motions by leveraging both speech and textual inputs. The key contributions are:

1. T3M enables users to achieve better control over the holistic motion generated from audio through the utilization of textual inputs.
2. To enhance the diversity of textual input within the training dataset, the paper aligns video and text in a joint embedding using VideoCLIP, and utilizes video input for training and text descriptions for inference.
3. The results show that the proposed T3M framework significantly outperforms existing methods in terms of both quantitative and qualitative evaluations.

🙋 Q&A

[01] Introduction

1. What are the key challenges in speech-driven 3D motion synthesis?

• Speech signals tend to be high-dimensional, noisy, and subject to variability, while motion data often exhibit sparsity, discreteness, and adherence to physical laws.
• The connection between speech and motion is not deterministic and relies on factors like environment, emotions, and individual personalities.
• Traditional speech-to-motion systems use speech audio as the sole input, leading to imprecise and undesired motion synthesis due to limitations in the expressive capabilities of the audio signal.

2. How does T3M address these challenges?

• T3M enables accurate control of body-hand motion generation via provided text prompts, which is valuable for addressing the rigidity often observed in motions generated solely from speech.
• The controllability afforded by T3M facilitates the creation of more nuanced and realistic motion sequences, enhancing overall realism and expressiveness.

[02] Related Work

1. What are the key developments in motion generation from speech?

• Facial reconstruction research has explored 2D talking head generation and 3D talking head generation.
• Body and hand motion reconstruction research can be categorized into rule-based and learning-based methods.
• Despite these advancements, existing methods still encounter difficulties in achieving a balance between diverse and controllable motion.

2. How does video-text pre-training help in this domain?

• Video-text pre-training aims to utilize the complementary information in videos and textual inputs to improve performance on subsequent tasks.
• Approaches like VideoBERT, VideoCLIP, and Video-LLaMA have demonstrated promising results in video understanding and video question answering.
• In this paper, the authors leverage VideoCLIP to process both video and textual inputs during training and testing.

[03] Method

1. What are the key components of the T3M framework?

• Face Generation: Leverages a pre-trained wav2vec 2.0 model to extract semantic representations from speech and a decoder to reconstruct facial motion.
• Context Features Generation: Utilizes the video encoder from VideoCLIP to generate context features, which are then used in the multimodal fusion block.
• Body and Hand Motion Generation: Includes an audio feature encoder, a latent codebook design, and a multimodal fusion block that combines speech and context features.

2. How does T3M address the issue of limited textual diversity in the training dataset?

• The authors employ the video-language contrastive learning framework of VideoCLIP to process the video frames and use the resulting video features to replace textual features during T3M training.
• This approach enhances the diversity of textual descriptions within the training dataset and improves the performance of motion synthesis.

[04] Experiment

1. What are the key metrics used to evaluate the quality of the generated holistic motion?

• Reality Score (RS): Measures the realism of the generated body and hand motions using a binary classifier.
• Beat Consistency Score (BCS): Evaluates the motion-speech beat correlation (time consistency).

2. How does T3M perform compared to the baseline methods?

• When using a video prompt, T3M demonstrates superior performance in terms of both RS and BCS indicators compared to the baseline TalkSHOW.
• Even when using a random prompt, T3M outperforms TalkSHOW in terms of BCS, showing that the generated motions are more consistent with the audio.

[05] Conclusion and Limitation

1. What are the key contributions of the T3M framework?

• T3M enables users to achieve better control over the holistic motion generated from speech by utilizing textual inputs.
• The paper employs VideoCLIP to enhance the diversity of textual descriptions within the training dataset, improving the performance of motion synthesis.
• The results show that T3M significantly outperforms existing methods in both quantitative and qualitative evaluations.

2. What are the potential future improvements for T3M?

• Incorporating more advanced text and video encoders from a pretrained multimodal model that surpasses the capabilities of VideoCLIP could further enhance the performance of T3M.
• Expanding the training dataset to cover a wider variety of scenarios and contexts could also lead to improved performance of the T3M framework.