ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model

1S-Lab, Nanyang Technological University
2SenseTime, China
corresponding author


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.


Figure 1. Pipeline Overview.

ReMoDiffuse is a retrieval-augmented 3D human motion diffusion model. Benefiting from the extra knowledge from the retrieved samples, ReMoDiffuse is able to achieve high-fidelity on the given prompts.

Figure 2. Main components of the proposed ReMoDiffuse.

a) Hybrid retrieval database stores various features of each training data. The pre-processed text feature and relative difference of motion length are sent to calculate the similarity with the given language description. The most similar ones are fed into the semantics-modulated transformer (SMT), serving as additional clues for motion generation. b) Semantics-modulated transformer (SMT) incorporates \(N\) identical decoder layers, including a semantics-modulated attention (SMA) layer and an FFN layer. The figure shows the detailed architecture of SMA module. CLIP's extracted text features \(f_{prompt}\) from the given prompt, features \(R^t\) and \(R^m\) from the retrieved samples, and current motion features \(f_{\Theta}\) will further refine the noised motion sequence. c) Condition mixture. To synthesize diverse and realistic motion sequences, starting from the pure noised sample, the motion transformer repeatedly eliminates the noise. To better mix outputs under different combinations of conditions, we suggest a training strategy to find the optimal hyper-parameters \(w_1\), \(w_2\), \(w_3\) and \(w_4\).

Quantitative Results

Qualitative Results

Fantastic Human Generation Works 🔥

Motion Generation

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory

2D Human Generation

Text2Human: Text-Driven Controllable Human Image Generation

StyleGAN-Human: A Data-Centric Odyssey of Human Generation

3D Human Generation

EVA3D: Compositional 3D Human Generation from 2D Image Collections

AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

Motion Dataset

HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling

GTA-Human: Playing for 3D Human Recovery


      title   =   {ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model}, 
      author  =   {Zhang, Mingyuan and
                   Guo, Xinying and
                   Pan, Liang and
                   Cai, Zhongang and
                   Hong, Fangzhou and
                   Li, Huirong and
                   Yang, Lei and
                   Liu, Ziwei},
      year    =   {2023},
      journal =   {arXiv preprint arXiv:2304.01116},


This work is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

We referred to the project page of Nerfies when creating this project page.