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  • 1.
    Alexanderson, Simon
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH. Motorica AB, Sweden.
    Nagy, Rajmund
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Beskow, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Henter, Gustav Eje
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH. Motorica AB, Sweden.
    Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models2023In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 42, no 4, article id 44Article in journal (Refereed)
    Abstract [en]

    Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthesising human motion that co-occurs with audio, e.g., dancing and co-speech gesticulation, since motion is complex and highly ambiguous given audio, calling for a probabilistic description. Specifically, we adapt the DiffWave architecture to model 3D pose sequences, putting Conformers in place of dilated convolutions for improved modelling power. We also demonstrate control over motion style, using classifier-free guidance to adjust the strength of the stylistic expression. Experiments on gesture and dance generation confirm that the proposed method achieves top-of-the-line motion quality, with distinctive styles whose expression can be made more or less pronounced. We also synthesise path-driven locomotion using the same model architecture. Finally, we generalise the guidance procedure to obtain product-of-expert ensembles of diffusion models and demonstrate how these may be used for, e.g., style interpolation, a contribution we believe is of independent interest.

  • 2.
    Henter, Gustav Eje
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Alexanderson, Simon
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Beskow, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    MoGlow: Probabilistic and controllable motion synthesis using normalising flows2020In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 39, no 6, p. 1-14, article id 236Article in journal (Refereed)
    Abstract [en]

    Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics. This paper introduces a new class of probabilistic, generative, and controllable motion-data models based on normalising flows. Models of this kind can describe highly complex distributions, yet can be trained efficiently using exact maximum likelihood, unlike GANs or VAEs. Our proposed model is autoregressive and uses LSTMs to enable arbitrarily long time-dependencies. Importantly, is is also causal, meaning that each pose in the output sequence is generated without access to poses or control inputs from future time steps; this absence of algorithmic latency is important for interactive applications with real-time motion control. The approach can in principle be applied to any type of motion since it does not make restrictive, task-specific assumptions regarding the motion or the character morphology. We evaluate the models on motion-capture datasets of human and quadruped locomotion. Objective and subjective results show that randomly-sampled motion from the proposed method outperforms task-agnostic baselines and attains a motion quality close to recorded motion capture.

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  • 3. Sajadi, Behzad
    et al.
    Majumder, Aditi
    Hiwada, Kazuhiro
    Maki, Atsuto
    Raskar, Ramesh
    Switchable Primaries Using Shiftable Layers of Colof Filter Arrays2011In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 30, no 4Article in journal (Refereed)
  • 4.
    Valle-Perez, Guillermo
    et al.
    Univ Bordeaux, Ensta ParisTech, Bordeaux, France..
    Henter, Gustav Eje
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Beskow, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Holzapfel, Andre
    KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.
    Oudeyer, Pierre-Yves
    Univ Bordeaux, Ensta ParisTech, Bordeaux, France..
    Alexanderson, Simon
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Transflower: probabilistic autoregressive dance generation with multimodal attention2021In: ACM Transactions on Graphics, ISSN 0730-0301, E-ISSN 1557-7368, Vol. 40, no 6, article id 195Article in journal (Refereed)
    Abstract [en]

    Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two contributions to tackle this problem. First, we present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context, using a multimodal transformer encoder. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers. Using this dataset, we compare our new model against two baselines, via objective metrics and a user study, and show that both the ability to model a probability distribution, as well as being able to attend over a large motion and music context are necessary to produce interesting, diverse, and realistic dance that matches the music.

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