Attention-based Multimodal Feature Fusion for Dance Motion Generation

Kritsis, Kosmas; Γκιόκας, Άγγελος; Pikrakis, Aggelos; Katsouros, Vassilis

doi:10.1145/3462244.3479961

Cited by 1 publication

(2 citation statements)

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“…In this section we describe the applied experimental setup along with the different experimental scenarios that we employed for evaluating the performance of our proposed multimodal DCHC autoencoder in terms of realism and style consistency. Herein we extend our previous study [32] by training the considered multimodal architecture based on a curriculum learning strategy. Furthermore we provide a thorough assessment of our proposal by employing 2 related state-of-the-art frameworks, in order to generate numerous motion sequences that were used to conduct qualitative, quantitative and subjective evaluations.…”

Section: Audio-informed Dance Synthesis Evaluationmentioning

confidence: 83%

“…Furthermore, by employing an attention mechanism we fuse the latent representations of past skeletal poses and audio features, in order to stochastically generate novel, variable and complex motion patters, enhancing the overall creativity of our system. Extending our previous work [32], the main contributions of this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 92%

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DanceConv: Dance Motion Generation With Convolutional Networks

et al. 2022

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Automatically synthesizing dance motion sequences is an increasingly popular research task in the broader field of human motion analysis. Recent approaches have mostly used recurrent neural networks (RNNs), which are known to suffer from prediction error accumulation, usually limiting models to synthesize short choreographies of less than 100 poses. In this paper we present a multimodal convolutional autoencoder that combines 2D skeletal and audio information by employing an attention-based feature fusion mechanism, capable of generating novel dance motion sequences of arbitrary length. We first validate the ability of our system to capture the temporal context of dancing in a unimodal setting, by considering only skeletal features as input. According to 1440 rating answers provided by 24 participants in our initial user-study, we show that the optimal performance was presented by the model that was trained with input sequences of 500 poses. Based on this outcome, we train the proposed multimodal architecture with two different approaches, namely teacher-forcing and self-supervised curriculum learning, to deal with the autoregressive error accumulation phenomenon. In our evaluation campaign, we generate 1800 sequences and compare our method against two state-of-the-art approaches. Through qualitative and quantitative experiments we demonstrate the improvements introduced by the proposed multimodal architecture in terms of realism, motion diversity and multimodality, reducing the Fréchet Inception Distance (FID) metric value by 0.39. Subjective results confirm the effectiveness of our approach to synthesize diverse dance motion sequences, reporting a 6% increase in style consistency preference according to 1800 answers provided by 45 evaluators.

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Section: Audio-informed Dance Synthesis Evaluationmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 92%