DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework

Speech-driven gesture generation is an emerging domain within virtual human creation, where current methods predominantly utilize Transformer-based architectures that necessitate extensive memory and are characterized by slow inference speeds. In response to these limitations, we propose \textit{DiM-Gestures}, a novel end-to-end generative model crafted to create highly personalized 3D full-body gestures solely from raw speech audio, employing Mamba-based architectures. This model integrates a Mamba-based fuzzy feature extractor with a non-autoregressive Adaptive Layer Normalization (AdaLN) Mamba-2 diffusion architecture. The extractor, leveraging a Mamba framework and a WavLM pre-trained model, autonomously derives implicit, continuous fuzzy features, which are then unified into a singular latent feature. This feature is processed by the AdaLN Mamba-2, which implements a uniform conditional mechanism across all tokens to robustly model the interplay between the fuzzy features and the resultant gesture sequence. This innovative approach guarantees high fidelity in gesture-speech synchronization while maintaining the naturalness of the gestures. Employing a diffusion model for training and inference, our framework has undergone extensive subjective and objective evaluations on the ZEGGS and BEAT datasets. These assessments substantiate our model's enhanced performance relative to contemporary state-of-the-art methods, demonstrating competitive outcomes with the DiTs architecture (Persona-Gestors) while optimizing memory usage and accelerating inference speed.

Speech-driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture. The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model's superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system's usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology. Supplementary videos and code can be accessed at https://zf223669.github.io/Diffmotion-v2-website/