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ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model

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arxiv 2304.01116 v1 pith:DO4CINES submitted 2023-04-03 cs.CV

ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model

Mingyuan Zhang , Xinying Guo , Liang Pan , Zhongang Cai , Fangzhou Hong , Huirong Li , Lei Yang , Ziwei Liu This is my paper
classification cs.CV
keywords motiongenerationremodiffuseretrievaldatabasediverseknowledgemotions
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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3D human motion generation is crucial for creative industry. Recent advances rely on generative models with domain knowledge for text-driven motion generation, leading to substantial progress in capturing common motions. However, the performance on more diverse motions remains unsatisfactory. In this work, we propose ReMoDiffuse, a diffusion-model-based motion generation framework that integrates a retrieval mechanism to refine the denoising process. ReMoDiffuse enhances the generalizability and diversity of text-driven motion generation with three key designs: 1) Hybrid Retrieval finds appropriate references from the database in terms of both semantic and kinematic similarities. 2) Semantic-Modulated Transformer selectively absorbs retrieval knowledge, adapting to the difference between retrieved samples and the target motion sequence. 3) Condition Mixture better utilizes the retrieval database during inference, overcoming the scale sensitivity in classifier-free guidance. Extensive experiments demonstrate that ReMoDiffuse outperforms state-of-the-art methods by balancing both text-motion consistency and motion quality, especially for more diverse motion generation.

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Cited by 5 Pith papers

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  2. ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

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    ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.

  3. ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

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    ScaleMoGen applies next-scale autoregressive prediction to human motion generation with multi-scale skeletal-temporal bitwise token maps, reporting SOTA FID on HumanML3D.

  4. Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation

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    STREAM decouples text (via AdaLN) from music (via energy-based BEAM attention) to generate editable, musically aligned dance motions with a new annotated dataset and editability metric.

  5. MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation

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    A semantically aligned latent space plus multi-token cross-attention conditioning sets a new state of the art in text-to-human-motion generation on HumanML3D.