Pith. sign in

REVIEW 5 cited by

ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Mingyuan Zhang , Xinying Guo , Liang Pan , Zhongang Cai , Fangzhou Hong , Huirong Li , Lei Yang , Ziwei Liu This is my paper · ORCID
classification cs.CV
keywords motiongenerationremodiffuseretrievaldatabasediverseknowledgemotions
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.AI 2026-06 unverdicted novelty 7.0 of 10

    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.

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

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

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

    cs.CV 2025-12 conditional novelty 6.0 of 10

    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.

  4. MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MOST improves rare-prompt text-to-motion generation by retrieving key motion clips through a new temporal clip Banzhaf interaction and using them as diffusion prompts.

  5. Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A survey paper reviews multimodal generative AI and autoregressive LLMs for text-driven human motion generation, with comparative tables of models, datasets, and metrics.

Pith tools