# Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis

arXiv: https://arxiv.org/abs/2512.09418 · HTML: https://mlsanity.com/paper/2512.09418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 21 of 21)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer](https://mlsanity.com/paper/2204.03638/) (2022)
- [Video Probabilistic Diffusion Models in Projected Latent Space](https://mlsanity.com/paper/2302.07685/) (2023)
- [Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2303.00440/) (2023)
- [VIDM: Video Implicit Diffusion Models](https://mlsanity.com/paper/2212.00235/) (2022)
- [Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence](https://mlsanity.com/paper/2410.11199/) (2024)
- [LEO: Generative Latent Image Animator for Human Video Synthesis](https://mlsanity.com/paper/2305.03989/) (2023)
- [Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation](https://mlsanity.com/paper/2305.05424/) (2023)
- [Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis](https://mlsanity.com/paper/2303.12644/) (2023)
- [D'ARTAGNAN: Counterfactual Video Generation](https://mlsanity.com/paper/2206.01651/) (2022)
- [EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing](https://mlsanity.com/paper/2406.00808/) (2024)
- [Generalizable Implicit Motion Modeling for Video Frame Interpolation](https://mlsanity.com/paper/2407.08680/) (2024)
- [Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images](https://mlsanity.com/paper/2103.05745/) (2021)
- [GD-VDM: Generated Depth for better Diffusion-based Video Generation](https://mlsanity.com/paper/2306.11173/) (2023)
- [Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models](https://mlsanity.com/paper/2503.14966/) (2025)
- [Hybrid Video Diffusion Models with 2D Triplane and 3D Wavelet Representation](https://mlsanity.com/paper/2402.13729/) (2024)
- [Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data](https://mlsanity.com/paper/2402.01054/) (2024)
- [Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis](https://mlsanity.com/paper/2408.03035/) (2024)

## Abstract (arXiv)

Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the complexity of expert annotation, remains a major obstacle for deep learning methods. We propose the Motion Conditioned Diffusion Model (MCDM), a label-free latent diffusion framework that synthesises realistic echocardiography videos conditioned on self-supervised motion features. To extract these features, we design the Motion and Appearance Feature Extractor (MAFE), which disentangles motion and appearance representations from videos. Feature learning is further enhanced by two auxiliary objectives: a re-identification loss guided by pseudo appearance features and an optical flow loss guided by pseudo flow fields. Evaluated on the EchoNet-Dynamic dataset, MCDM achieves competitive video generation performance, producing temporally coherent and clinically realistic sequences without reliance on manual labels. These results demonstrate the potential of self-supervised conditioning for scalable echocardiography synthesis. Our code is available at https://github.com/ZheLi2020/LabelfreeMCDM.