Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations. MotionGPT3 exemplifies the latter paradigm, combining a learned continuous motion latent space with a diffusion-based prior for text-conditioned synthesis. While rectified flow objectives have recently demonstrated favorable convergence and inference-time properties relative to diffusion in image and audio generation, it remains unclear whether these advantages transfer cleanly to the motion generation setting. In this work, we conduct a controlled empirical study comparing diffusion and rectified flow objectives within the MotionGPT3 framework. By holding the model architecture, training protocol, and evaluation setup fixed, we isolate the effect of the generative objective on training dynamics, final performance, and inference efficiency. Experiments on the HumanML3D dataset show that rectified flow converges in fewer training epochs, reaches strong test performance earlier, and matches or exceeds diffusion-based motion quality under identical conditions. Moreover, flow-based priors exhibit stable behavior across a wide range of inference step counts and achieve competitive quality with fewer sampling steps, yielding improved efficiency-quality trade-offs. Overall, our results suggest that several known benefits of rectified flow objectives do extend to continuous-latent text-to-motion generation, highlighting the importance of the training objective choice in motion priors.
Figures & tables
Figure 1: Overview of the MotionGPT-3 architecture with alternative motion priors. The dual-stream GPT-2 backbone produces text-conditioned motion latents, which are fed into either a diffusion-based or flow-based motion prior. The and symbols indicate frozen and trainable modules, respectively.
Methods
R@1 ↑
R@2 ↑
R@3 ↑
FID ↓
MMDist ↓
Diversity ↑
MModality ↑
Real
0.511
0.703
0.797
0.002
2.974
9.503
–
T2M-GPT ( Zhang et al., 2023 )
0.491
0.680
0.775
0.116
3.118
9.761
1.856
DiverseMotion ( Lou et al., 2023 )
0.515
0.706
0.802
0.072
2.941
9.683
1.869
MoMask ( Guo et al., 2023 )
0.521
0.713
0.807
0.045
2.958
9.620
1.241
MotionGPT ( Jiang et al., 2023 )
0.492
0.681
0.733
0.232
3.096
9.528
2.00
TM2T ( Guo et al., 2022b )
0.424
0.618
0.729
1.501
3.467
8.589
2.424
Table 1: Comparison of text-to-motion generation methods on HumanML3D. Arrows indicate whether higher ( ↑ ) or lower ( ↓ ) values are better. Real denotes statistics computed from ground-truth motion data. Bold values indicate the best performance for each metric.
Figure 2: Motion-prior-only inference Pareto comparison between diffusion and flow variants, illustrating the trade-off between inference time and generation quality.
Figure 3: End to end Pareto comparison between diffusion and flow variants, illustrating the trade-off between end-to-end inference time and generation quality.
Figure 4: Validation metrics across training epochs for diffusion- and flow-based variants. We report FID, Matching Score, and R-Precision (R@3). Faded curves indicate raw validation measurements, while solid curves show exponential moving averages computed with a span of five epochs to highlight overall training trends.
Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuous or discrete motion representations. However, continuous representations entangle semantics with dynamics, while discrete representations lose fine-grained motion details. In this context, we propose FlowCoMotion, a novel motion generation framework that unifies both treatments from a modeling perspective. Specifically, FlowCoMotion employs token-latent coupling to capture both semantic content and high-fidelity motion details. In the latent branch, we apply multi-view distillation to regularize the continuous latent space, while in the token branch we use discrete temporal resolution quantization to extract high-level semantic cues. The motion latent is then obtained by combining the representations from the two branches through a token-latent coupling network. Subsequently, a velocity field is predicted based on the textual conditions. An ODE solver integrates this velocity field from a simple prior, thereby guiding the sample to the potential state of the target motion. Extensive experiments show that FlowCoMotion achieves competitive performance on text-to-motion benchmarks, including HumanML3D and SnapMoGen.
Dawei Guan, Di Yang, Chengjie Jin +1
School of Artificial Intelligence and Data Science, University of Science and Technology of China · Suzhou Institute for Advanced Research, University of Science and Technology of China
Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimensional, temporally downsampled latent spaces learned primarily for reconstruction, a bottleneck that can limit generation quality and preclude direct manipulation of individual frames and joints. We introduce MotionSpaceFlow (MSFlow), a representation-aware flow-matching framework that predicts clean motion directly in continuous motion space without a learned encoder or decoder. To account for the anisotropic structure of direct motion representations, we propose representation-aware noise scaling and show how the initial Gaussian source scale governs the covariance of intermediate probability-path marginals. We further introduce a Representation-Aware Multimodal Diffusion Transformer (RA-MMDiT), which jointly updates token-level language and full-resolution motion features through joint attention while adapting temporal information flow to the motion representation: causal attention for incremental features defined by frame-to-frame changes, and bidirectional attention for global features such as absolute joint coordinates. Across different datasets and motion representations, MSFlow achieves state-of-the-art text-to-motion performance. Its global representation variant additionally enables zero-shot, inference-time control over any joint or frame through projection sampling without control-conditioned training, delivering leading motion quality with exact constraint satisfaction.
We introduce MoLingo, a text-to-motion (T2M) model that generates realistic, lifelike human motion by denoising in a continuous latent space. Recent works perform latent space diffusion, either on the whole latent at once or auto-regressively over multiple latents. In this paper, we study how to make diffusion on continuous motion latents work best. We focus on two questions: (1) how to build a semantically aligned latent space so diffusion becomes more effective, and (2) how to best inject text conditioning so the motion follows the description closely. We propose a semantic-aligned motion encoder trained with frame-level text labels so that latents with similar text meaning stay close, which makes the latent space more diffusion-friendly. We also compare single-token conditioning with a multi-token cross-attention scheme and find that cross-attention gives better motion realism and text-motion alignment. With semantically aligned latents, auto-regressive generation, and cross-attention text conditioning, our model sets a new state of the art in human motion generation on standard metrics and in a user study. We will release our code and models for further research and downstream usage.
Yannan He, Garvita Tiwari, Xiaohan Zhang +4
University of Tübingen, Germany · Tübingen AI Center, Germany · Max Planck Institute for Informatics, Saarland Informatics Campus, Germany +2