Didn't see that coming: a survey on non-verbal social human behavior forecasting
원문 초록
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarised and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues.
함께 살펴볼 논문
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MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty
BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation
SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios
EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models
MMGT: Motion Mask Guided Two-Stage Network for Co-Speech Gesture Video Generation
Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
토론
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