Paper Reading AI Learner

Towards Variable and Coordinated Holistic Co-Speech Motion Generation

2024-03-30 13:41:57
Yifei Liu, Qiong Cao, Yandong Wen, Huaiguang Jiang, Changxing Ding

Abstract

This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars, focusing on two key aspects: variability and coordination. Variability allows the avatar to exhibit a wide range of motions even with similar speech content, while coordination ensures a harmonious alignment among facial expressions, hand gestures, and body poses. We aim to achieve both with ProbTalk, a unified probabilistic framework designed to jointly model facial, hand, and body movements in speech. ProbTalk builds on the variational autoencoder (VAE) architecture and incorporates three core designs. First, we introduce product quantization (PQ) to the VAE, which enriches the representation of complex holistic motion. Second, we devise a novel non-autoregressive model that embeds 2D positional encoding into the product-quantized representation, thereby preserving essential structure information of the PQ codes. Last, we employ a secondary stage to refine the preliminary prediction, further sharpening the high-frequency details. Coupling these three designs enables ProbTalk to generate natural and diverse holistic co-speech motions, outperforming several state-of-the-art methods in qualitative and quantitative evaluations, particularly in terms of realism. Our code and model will be released for research purposes at this https URL.

Abstract (translated)

本文解决了为3D虚拟角色生成逼真度高的整体协同运动的问题,重点关注两个关键方面:可变性和协调性。可变性使得虚拟角色即使拥有相似的语音内容,也能表现出广泛的动作,而协调性确保了面部表情、手势和身体姿态之间的和谐对齐。我们希望通过ProbTalk,一个为共同建模面部、手和身体运动而设计的统一概率框架来实现这一目标。ProbTalk借鉴了变分自编码器(VAE)架构,并包括三个核心设计。首先,我们将产品量化(PQ)引入VAE,从而丰富复杂整体运动的表示。其次,我们设计了一个新型的非自回归模型,将2D位置编码嵌入产品量化表示中,从而保留PQ代码的 essential结构信息。最后,我们采用二级阶段来微调初步预测,进一步锐化高频细节。将这三个设计相结合使得ProbTalk能够生成自然且多样化的整体协同运动,在质量和数量评估中超过了最先进的方法,尤其是在逼真度方面。我们的代码和模型将在此处https://url.com/释放研究用途。

URL

https://arxiv.org/abs/2404.00368

PDF

https://arxiv.org/pdf/2404.00368.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot