You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Motion Latent Diffusion (MLD) is a **text-to-motion** and **action-to-motion** diffusion model. Our work achieves **state-of-the-art** motion quality and two orders of magnitude **faster** than previous diffusion models on raw motion data.
11
10
@@ -14,19 +13,18 @@ Motion Latent Diffusion (MLD) is a **text-to-motion** and **action-to-motion** d
14
13
</p>
15
14
16
15
## 🚩 News
17
-
18
-
02/Feb/2023 - release action-to-motion task, please refer to [the config](https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml) and [the pre-train model](https://drive.google.com/file/d/1G9O5arldtHvB66OPr31oE_rJG1bH_R39/view)
19
-
20
-
18/Jan/2023 - add a detailed [readme](https://github.com/ChenFengYe/motion-latent-diffusion/tree/main/configs) of the configuration
21
-
22
-
09/Jan/2023 - release [no VAE config](https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_novae_humanml3d.yaml) and [pre-train model](https://drive.google.com/file/d/1_mgZRWVQ3jwU43tLZzBJdZ28gvxhMm23/view), you can use MLD framework to train diffusion on raw motion like [MDM](https://github.com/GuyTevet/motion-diffusion-model).
23
-
24
-
22/Dec/2022 - first release, demo, and training for text-to-motion
25
-
26
-
08/Dec/2022 - upload paper and init project, code will be released in two weeks
16
+
-[2023/02/28] MLD got accepted by CVPR 2023!
17
+
-[2023/02/02] release action-to-motion task, please refer to [the config](https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_mld_humanact12.yaml) and [the pre-train model](https://drive.google.com/file/d/1G9O5arldtHvB66OPr31oE_rJG1bH_R39/view)
18
+
-[2023/01/18] add a detailed [readme](https://github.com/ChenFengYe/motion-latent-diffusion/tree/main/configs) of the configuration
19
+
-[2023/01/09] release [no VAE config](https://github.com/ChenFengYe/motion-latent-diffusion/blob/main/configs/config_novae_humanml3d.yaml) and [pre-train model](https://drive.google.com/file/d/1_mgZRWVQ3jwU43tLZzBJdZ28gvxhMm23/view), you can use MLD framework to train diffusion on raw motion like [MDM](https://github.com/GuyTevet/motion-diffusion-model).
20
+
-[2022/12/22] first release, demo, and training for text-to-motion
21
+
-[2022/12/08] upload paper and init project, code will be released in two weeks
Refer to [TEMOS-Rendering motions](https://github.com/Mathux/TEMOS) for blender setup, then install the following dependencies.
150
149
@@ -206,18 +205,24 @@ Our model is capable of generating motions with arbitrary lengths. To handle dif
206
205
207
206
</details>
208
207
208
+
<details>
209
+
<summary><b>MLD-1 VS MLD-7</b></summary>
210
+
MLD-7 only works best in evaluating VAE models (Tab. 4), and MLD-1 wins these generation tasks (Tab. 1, 2, 3, 6). In other words, MLD-7 wins the first training stage for the VAE part, while MLD-1 wins the second for the diffusion part. We thought MLD-7 should perform better than MLD-1 in several tasks, but the results differ. The main reason for this downgrade of a larger latent size, we believe, is the small amount of training data. HumanML3D only includes 15k motion sequences, much smaller than billions of images in image generation. MLD-7 could work better when the motion data amount reaches the million level.
211
+
</details>
212
+
209
213
**[Details of configuration](./configs)**
210
214
211
215
## Citation
212
216
213
217
If you find our code or paper helps, please consider citing:
214
218
215
-
```
216
-
@article{chen2022mld,
217
-
author = {Xin, Chen and Jiang, Biao and Liu, Wen and Huang, Zilong and Fu, Bin and Chen, Tao and Yu, Jingyi and Yu, Gang},
218
-
title = {Executing your Commands via Motion Diffusion in Latent Space},
219
-
journal = {arXiv},
220
-
year = {2022},
219
+
```bibtex
220
+
@inproceedings{chen2023mld,
221
+
title = {Executing your Commands via Motion Diffusion in Latent Space},
222
+
author = {Xin, Chen and Jiang, Biao and Liu, Wen and Huang, Zilong and Fu, Bin and Chen, Tao and Yu, Jingyi and Yu, Gang},
223
+
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
0 commit comments