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+++ b/README.md
@@ -240,7 +240,7 @@ Paper by Folder : [đ/survey](https://github.com/OpenHuman-ai/awesome-gesture_
- DiffuGesture: Generating Human Gesture From Two-person Dialogue With Diffusion Models [[paper]](https://openreview.net/pdf?id=swc28UDR8Wk)
- The FineMotion entry to the GENEA Challenge 2023: DeepPhase for conversational gestures generation [[paper]](https://openreview.net/pdf?id=pVBKLqpAUtP)
- Am I listening - Evaluating theQuality of Generated Data-driven Listening Motion [[paper]](https://pieterwolfert.com/files/icmi_2023.pdf)
-- Unified speech and gesture synthesis using flow matching [[paper]](https://arxiv.org/pdf/2310.05181.pdf) ; [[homepage]](https://shivammehta25.github.io/Match-TTSG/) ;
+- Unified speech and gesture synthesis using flow matching [[paper]](https://arxiv.org/pdf/2310.05181.pdf) ; [[homepage]](https://shivammehta25.github.io/Match-TTSG/) ;
@@ -380,11 +380,11 @@ Paper by Folder : [đ/survey](https://github.com/OpenHuman-ai/awesome-gesture_
-## 3. Selected Approachs
+## 3. Approachs
-### 3.1 Selected rule Base approach
+### 3.1 Rule Base approach
- [1994] Rule-based generation of facial expression, gesture & spoken intonation for multiple conversational agents [[paper]]()
@@ -458,10 +458,6 @@ This section is -- **not accurate** --> continue edditing
- Freeform Body Motion Generation from Speech [[paper]](https://arxiv.org/pdf/2203.02291) ; [[TheTempAccount/Co-Speech-Motion-Generation]](https://github.com/TheTempAccount/Co-Speech-Motion-Generation) ; [[youtube]](https://www.youtube.com/watch?v=Wb5VYqKX_x0)
- ăCVMP 2021ă **Flow-VAE** Speech-Driven Conversational Agents using Conditional Flow-VAEs [[paper]]()
- - **VQ-VAE**
-
- -
-
- **Learnable noise codes**
- ăICCV 2021ă Speech Drives Templates: Co-Speech Gesture Synthesis With Learned Templates ; [[paper]](https://arxiv.org/pdf/2108.08020.pdf) ; [[ShenhanQian/SpeechDrivesTemplates]](https://github.com/ShenhanQian/SpeechDrivesTemplates) ;
@@ -494,7 +490,7 @@ This section is -- **not accurate** --> continue edditing
- ăCVPR 2022ă Audio-Driven Neural Gesture Reenactment With Video Motion Graphs [[paper]]()
- ăAAMAS 2022ă Multimodal analysis of the predictability of hand-gesture properties [[paper]]()
- ăICMI 2022ă **GestureMaster** GestureMaster: Graph-based Speech-driven Gesture Generation [[paper]]()
- - ăICCV 2021ă Speech Drives Templates: Co-Speech Gesture Synthesis With Learned Templates [[paper]]() ; [shenhanqian/speechdrivestemplates](https://github.com/shenhanqian/speechdrivestemplates) ; [[youtube]]() ; [poster](https://shenhanqian.com/assets/2021-07-25-sdt/poster.pdf)
+
- ăICCV 2021ă **Audio2Gestures** Audio2Gestures: Generating Diverse Gestures From Speech Audio With Conditional Variational Autoencoders [[paper]]()
- ăIVA 2021ă Speech2Properties2Gestures: Gesture-Property Prediction as a Tool for Generating Representational Gestures from Speech [[paper]]() ; [[homepage]]()
- ăECCV 2020ă **Mix-StAGE** Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach [[paper]]()
@@ -520,6 +516,10 @@ This section is -- **not accurate** --> continue edditing
## 5. Learning Objective
+- [**Fréchet Inception Distance (FID)**](https://arxiv.org/abs/1706.08500) -
+- [**Fréchet Gesture Distance (FGD)**](https://arxiv.org/abs/2009.02119) -
+- [**Fréchet Template Distance (FTD)**](https://arxiv.org/abs/2108.08020) -
+
| Full name | Description |
| ------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Adversarial Loss (**Adv**) | Used in Generative Adversarial Networks (GANs), this loss function pits a generator network against a discriminator network, with the goal of the generator producing samples that can fool the discriminator into thinking they are real. |
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