# PMG: Progressive Motion Generation via Sparse Anchor Postures Curriculum Learning

arXiv: https://arxiv.org/abs/2504.16722 · HTML: https://mlsanity.com/paper/2504.16722/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 45 of its 61 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model](https://mlsanity.com/paper/2404.19759/) (2024)
- [FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing](https://mlsanity.com/paper/2312.15004/) (2023)
- [HumanTOMATO: Text-aligned Whole-body Motion Generation](https://mlsanity.com/paper/2310.12978/) (2023)
- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [TLControl: Trajectory and Language Control for Human Motion Synthesis](https://mlsanity.com/paper/2311.17135/) (2023)
- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation](https://mlsanity.com/paper/2304.10417/) (2023)
- [MotionFix: Text-Driven 3D Human Motion Editing](https://mlsanity.com/paper/2408.00712/) (2024)
- [Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/1905.05816/) (2019)
- [Taming Diffusion Probabilistic Models for Character Control](https://mlsanity.com/paper/2404.15121/) (2024)
- [Aligning Language Models to Explicitly Handle Ambiguity](https://mlsanity.com/paper/2404.11972/) (2024)
- [StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework](https://mlsanity.com/paper/2405.05691/) (2024)
- [Motion Anything: Any to Motion Generation](https://mlsanity.com/paper/2503.06955/) (2025)
- [Curriculum Self-Paced Learning for Cross-Domain Object Detection](https://mlsanity.com/paper/1911.06849/) (2019)
- [Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion](https://mlsanity.com/paper/2312.04466/) (2023)
- [MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm](https://mlsanity.com/paper/2502.02358/) (2025)
- [Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning](https://mlsanity.com/paper/2405.07490/) (2024)

## Abstract (arXiv)

In computer animation, game design, and human-computer interaction, synthesizing human motion that aligns with user intent remains a significant challenge. Existing methods have notable limitations: textual approaches offer high-level semantic guidance but struggle to describe complex actions accurately; trajectory-based techniques provide intuitive global motion direction yet often fall short in generating precise or customized character movements; and anchor poses-guided methods are typically confined to synthesize only simple motion patterns. To generate more controllable and precise human motions, we propose ProMoGen (Progressive Motion Generation), a novel framework that integrates trajectory guidance with sparse anchor motion control. Global trajectories ensure consistency in spatial direction and displacement, while sparse anchor motions only deliver precise action guidance without displacement. This decoupling enables independent refinement of both aspects, resulting in a more controllable, high-fidelity, and sophisticated motion synthesis. ProMoGen supports both dual and single control paradigms within a unified training process. Moreover, we recognize that direct learning from sparse motions is inherently unstable, we introduce SAP-CL (Sparse Anchor Posture Curriculum Learning), a curriculum learning strategy that progressively adjusts the number of anchors used for guidance, thereby enabling more precise and stable convergence. Extensive experiments demonstrate that ProMoGen excels in synthesizing vivid and diverse motions guided by predefined trajectory and arbitrary anchor frames. Our approach seamlessly integrates personalized motion with structured guidance, significantly outperforming state-of-the-art methods across multiple control scenarios.