Update README.md

Author: SylvainFaisan

README.md

@@ -13,13 +13,18 @@ We test our method on two commonly used facial expression datasets, [**CoMA**](h
 
 ### 3.1 Label control
 
+We perform a conditional generation according to the expression label y. 
+
 Examples
 
 
  <img src="results/angry.gif" width="30%" height="30%" /> <img src="results/eyebrow.gif" width="30%" height="30%"  />  <img src="results/mouth_extreme.gif"  width="30%" height="30%"  /> <img src="results/disgust.gif" width="30%" height="30%"  />  <img src="results/mouth_open.gif"  width="30%" height="30%"  /> <img src="results/lips_up_2.gif"  width="30%" height="30%" /> 
 
 
 ### 3.2 Text control
+We perform a conditional generation according to a text. Note that the input texts “disgust high smile” and “angry mouth down” are the combinations of two terms used for training. For instance, “disgust high smile” is a new description that hasn’t been seen before, which combines “disgust” and “high smile”.
+
+
 
 Text to expression examples:
 
@@ -32,6 +37,8 @@ Text to expression examples:
 
 ### 3.3 Sequence filling
 
+Similarly to inpainting whose purpose is to predict missing pixels of an image using a mask region as a condition, this task aims to predict missing frames of a temporal sequence by leveraging known frames as a condition.
+
 #### Filling from the beginning.
  <img src="results/ffb_1.gif" width="30%" height="30%"  />  <img src="results/ffb_2.gif"  width="30%" height="30%"  />
 
@@ -44,8 +51,13 @@ Text to expression examples:
 
 ### 3.4 Diversity
 
+The specific aim of the 3D facial animation generation is to learn a model that can generate facial expressions that are realistic, appearance-
+preserving, rich in diversity, with various ways to condition it. 
+
 #### Diversity of label control
 
+The diversity of the generated sequences in terms of expression is shown hereafter. The meshes are obtained by retargeting the expression of the generated 𝑥0 on the same neutral faces.
+
 mouth side
 
 <img src="results/mouth_side_d.gif"  width="50%" height="50%"  /> 
@@ -55,6 +67,8 @@ mouth up
 <img src="results/mouth_up_d.gif"  width="50%" height="50%"  /> 
 
 #### Diversity of Geometry-adaptive generation
+In the Geometry-adaptive generation task, we generate a facial expression from a given facial anatomy. This task can also be guided by a classifier. In order to benefit from the consistent and quality expressions adapted to the facial morphology by the DDPM, one can extract a landmark set 𝐿 from a mesh 𝑀, perform the geometry-adaptive task on it to generate a sequence involving 𝐿, and retarget it to 𝑀 by the landmark-guided mesh deformation. We show hereafter the diversity of the generated sequences.
+
 
 eyebrow
 
@@ -68,9 +82,6 @@ lips up
 ### 3.5 Comparison
 #### Label control
 
-We perform a conditional generation according to the expression label y. 
-Conditioning the reverse process of an unconditional DDPM is achieved here by using classifier-guidance. 
-
 "high smile"
 
  <img src="results/comp_high_smile.gif"  width="50%" height="50%"  /> 
@@ -91,6 +102,8 @@ Conditioning the reverse process of an unconditional DDPM is achieved here by us
 
  ### 3.6 Expression retargeting
 
+The landmark sequence taken from a full sequence of the CoMA dataset is retargeted onto several facial meshes.
+
  <img src="results/exp_retarget.gif" width="50%" height="50%"  /> 
 
 ## 4. Code