new projects

_gsocprojects/2024/project_ArtExtract.md

@@ -0,0 +1,10 @@
+---
+project: ArtExtract
+layout: default
+logo: placeholder.jpg
+description: |
+   The project aims at using artificial intelligence in combination with multi-spectral imaging techniques to gain insight in ancient paintings and other form of fina art. 
+
+---
+
+{% include gsoc_project.ext %}

_gsocprojects/2024/project_ChoreoAI.md

@@ -0,0 +1,10 @@
+---
+project: Choreo-AI
+layout: default
+logo: placeholder.jpg
+description: |
+   This project will expand the state-of-the-art in this intersectional field by exploring duets featuring pairs of dancers, enabling choreography that features authentic interactions between humans & AI models.
+
+---
+
+{% include gsoc_project.ext %}

_gsocprojects/2024/project_Sample.md

@@ -1,9 +1,9 @@
 ---
-project: YourProjectName
+project: TBC
 layout: default
 logo: placeholder.jpg
 description: |
-   Add 2 or 3 sentences describing your project.
+   Project TBC
 
 ---
 

_gsocproposals/2024/proposal_ChoreoAI1.md

@@ -0,0 +1,44 @@
+---
+title: AI-Generated Choreography: from Solos to Duets
+layout: gsoc_proposal
+project: Choreo-AI
+year: 2024
+organization:
+  - LBNL
+  - Northeastern
+
+---
+
+## Description
+
+While the fields of technology and dance have historically not often intersected, recent years have seen the advent of AI-generated choreography using models trained on motion capture of a single dancer ([https://arxiv.org/abs/1907.05297](https://arxiv.org/abs/1907.05297)). This project will expand the state-of-the-art in this intersectional field by exploring duets featuring pairs of dancers, enabling choreography that features authentic interactions between humans & AI models.
+
+## Duration
+Total project length: 175 hours
+
+## Task ideas
+ * Extract pose information from curated videos of dance duets
+ * Train a GNN and/or Transformer model to analyze this data and generate new duet interaction ideas
+
+## Expected results
+ * Create a dataset of dynamic point-cloud data corresponding to extracted motion capture poses from videos of dance duets
+ * Train an AI model that can generate the movements of Dancer #2 conditioned on the inputs of Dancer #1 and/or invent new, physically-plausible duet phrases
+ * If time permits: Learn key relationships between parts of the body of each dancer that are integral to the dynamics of the duet
+ * We will collaborate with the original dancers to use the model outputs to inspire new performance material
+
+## Requirements
+Participants should be comfortable with standard data science software including Python, Git, Numpy, Matplotlib, and Pandas. Previous experience in Machine Learning, either in TensorFlow or PyTorch, is preferred. While previous experience in dance or the performing arts is not needed, an interest in the artistic and open-ended aesthetic dimensions of the project is required. Strong interpersonal & communication skills are essential.
+
+## Project difficulty level
+Hard
+
+## Mentors
+  * [Mariel Pettee](mailto:[email protected]) (Lawrence Berkeley National Laboratory)
+  * [Ilya Vidrin](mailto:[email protected]) (Northeastern University)
+
+
+
+
+Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you.
+
+

_gsocproposals/2024/proposal_PaintingInAPainting.md

@@ -0,0 +1,48 @@
+---
+title:  ArtExtract
+layout: gsoc_proposal
+project: Painting in a Painting
+year: 2024
+organization:
+  - Alabama
+
+---
+
+## Description
+
+In recent years advanced multispectral and X-ray imaging techniques have allowed art historians and preservation experts to find hidden images beneath famous paintings. These hidden images can have different origins. In some cases they are older paintings or sketches on a canvas that the artist decided to reuse for another painting. Canvases were often resued to save money. In other cases modifications ot the original painting are added to conceal controversial details, or just because the artist changet their mind. We porpose to use artificial intelligence in combination with high quality multispectral data to simplify, automatize, and improve the process of analyzing a painting, indentify its properties and find hidden images.  
+
+## Duration
+
+Total project length: 175 hours
+
+## Task ideas
+ * Extract useful information from multispectral images of paintings, pigment type, damage, previous restorations.
+ * Find if the painting contains a hidden image that has been painted over.
+
+## Expected results
+ * Prepare a dataset of multi spectral images of paintings.
+ * Train a CNN to identify painting properties
+ * Train a CNN to identify images hidden beneath paintings
+ * (If time premits) train an AI model to retrieve the hidden image.
+
+## Requirements
+ * Python
+ * PyTorch/TensorFlow
+ * Experience with CNNs
+
+
+## Project difficulty level
+Medium
+
+## Mentors
+  * [Emanuele Usai](mailto:[email protected]) (Alabama)
+  * [Sergei Gleyzer](mailto:[email protected]) (Alabama)
+  * [TBC](mailto:[email protected]) (TBC)
+
+
+
+
+Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you.
+
+

_gsocproposals/2024/proposal_Sample1.md

@@ -1,7 +1,7 @@
 ---
-title:  Your Proposal Title
+title:  TBC
 layout: gsoc_proposal
-project: YourProjectName
+project: TBC
 year: 2024
 organization:
   - YourInstitute
@@ -10,7 +10,7 @@ organization:
 
 ## Description
 
-Add 2-3 sentences describing the proposal
+Proposal TBC
 
 ## Duration
 
@@ -23,15 +23,15 @@ Total project length: 175 hours
  * Add expected result 1
  * Add expected result 2 etc
 
-
-
 ## Requirements
 Add requirements here
 
+## Project difficulty level
+Easy/Medium/Hard
 
 ## Mentors
-  * [Mentor Name]((mailto:[email protected])) (Mentor institute)
-  * [Mentor Name 2]((mailto:[email protected])) (Mentor institute 2)
+  * [Mentor Name](mailto:[email protected]) (Mentor institute)
+  * [Mentor Name 2](mailto:[email protected]) (Mentor institute 2)