Speech-driven 3D facial animation aims at generating facial movements that are synchronized with the driving speech, which has been widely explored recently. Existing works mostly neglect the person-specific talking style in generation, including facial expression and head pose styles. Several works intend to capture the personalities by fine-tuning modules. However, limited training data leads to the lack of vividness. In this work, we proposeAdaMesh, a novel adaptive speech-driven facial animation approach, which learns the personalized talking style from a reference video of about 10 seconds and generates vivid facial expressions and head poses. Specifically, we propose mixture-of-low-rank adaptation (MoLoRA) to fine-tune the expression adapter, which efficiently captures the facial expression style. For the personalized pose style, we propose a pose adapter by building a discrete pose prior and retrieving the appropriate style embedding with a semantic-aware pose style matrix without fine-tuning. Extensive experimental results show that our approach outperforms state-of-the-art methods, preserves the talking style in the reference video, and generates vivid facial animation.

Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion matching framework. Specifically, we first present a gesture VQ-VAE module to learn a codebook to summarize meaningful gesture units. With each code representing a unique gesture, random jittering problems are alleviated effectively. We then use Levenshtein distance to align diverse gestures with different speech. Levenshtein distance based on audio quantization as a similarity metric of corresponding speech of gestures helps match more appropriate gestures with speech, and solves the alignment problem of speech and gestures well. Moreover, we introduce phase to guide the optimal gesture matching based on the semantics of context or rhythm of audio. Phase guides when text-based or speech-based gestures should be performed to make the generated gestures more natural. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, database, pre-trained models and demos are available at https://github.com/YoungSeng/QPGesture.

Audio-driven talking face with portrait customization enhances the flexibility of avatar applications for different scenarios, such as on-line meetings, mixed reality, and data generation. Among the existing methods, audio-driven talking face and face swapping are typically viewed as separate tasks that are cascaded to achieve the objective. Using state-of-the-art methods Wav2Lip and SimSwap for this purpose, we meet some issues: affected mouth synchronization, lost texture information, and slow inference speed. To resolve these issues, we propose an end-to-end model that combines the advantages of both approaches. Our approach generates highly-synchronized mouth with the aid of a pre-trained lip-sync discriminator. And identity information is provided by ArcFace and the ID injection module in the model because of its strong correlation with facial texture. Experimental results demonstrate that our method achieves lip-sync accuracy comparable to real synced videos, preserves more texture details than cascade methods, and alleviates the blurring of Wav2Lip. Also, our approach improves the inference speed.1

The art of communication beyond speech there are gestures. The automatic co-speech gesture generation draws much attention in computer animation. It is a challenging task due to the diversity of gestures and the difficulty of matching the rhythm and semantics of the gesture to the corresponding speech. To address these problems, we present DiffuseStyleGesture, a diffusion model based speech-driven gesture generation approach. It generates high-quality, speech-matched, stylized, and diverse co-speech gestures based on given speeches of arbitrary length. Specifically, we introduce cross-local attention and self-attention to the gesture diffusion pipeline to generate better speech matched and realistic gestures. We then train our model with classifier-free guidance to control the gesture style by interpolation or extrapolation. Additionally, we improve the diversity of generated gestures with different initial gestures and noise. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, pre-trained models, and demos are available at https://github.com/YoungSeng/DiffuseStyleGesture.

Deep Neural Networks suffer significant performance degeneration when noisy labels corrupt latent data representations. Previous work has attempted to alleviate this problem by exploiting contrastive learning, the pair building of which is critical. However, existing methods either conduct sample-level processes and then use the resultant subset to construct pairs or directly perform pair-level selecting using a fixed threshold, both leading to sub-optimal pairing and subsequent representation learning. To address this issue, we propose a novel adaptive contrastive learning method (ACL) working at the pair level to select contrastive pairs adaptively. Specifically, we consider the model's learning status to adjust the confidence threshold in a self-adaptive manner instead of fixing it. Then, towards the ineffectiveness of the thresholding method on unconfident pairs, we automatically apply instance-specific temperature to boost the confidence of accurately-predicted samples and their pairs. We further introduce temporal cross-ensembling to handle the impact of noisy labels on model predictions. As a result, diverse pairs are correctly selected for contrastive learning to induce discriminative representations robust to various types of label noise. Extensive experimental results on several standard benchmarks and real-world datasets indicate the superiority of ACL, especially in extremely noisy scenarios.

This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA challenge provides the processed datasets and performs crowdsourced evaluations to compare the performance of different gesture generation systems. In this paper, we explore an automatic gesture generation system based on multimodal representation learning. We use WavLM features for audio, FastText features for text and position and rotation matrix features for gesture. Each modality is projected to two distinct subspaces: modality-invariant and modality-specific. To learn inter-modality-invariant commonalities and capture the characters of modality-specific representations, gradient reversal layer based adversarial classifier and modality reconstruction decoders are used during training. The gesture decoder generates proper gestures using all representations and features related to the rhythm in the audio. Our code, pre-trained models and demo are available at https://github.com/YoungSeng/ReprGesture.