This image was taken from State of the Art on Neural Rendering by Tewari et al.


Here is the collection of final projects.

Title Authors link
DeepMTV Daniel Bronstein, Kelvin Kang link
InterFaceGAN Tomas Cabezon Pedroso link
Open-domain Compositional Image Editing with Text Qiyun Chen, Gaoyue Zhou, Zhizhuo Zhou link
Curve-based Image Synthesis Sean Chen link
Try-on GAN Lena Du, Chenguang Deng, Zhong Zheng link
3D Mesh generation Nikolas Diamant, Himalini Gururaj, Shiva Peri link
GANs for Coarse Style and Scene Data Augmentation Harry Freeman, Tiffany Ma link
Controllable Composition of Visual Relations via Object Abstractions Nikolaos Gkanatsios link
Semantic Chameleon Effects Violet Han link
Synthetic data generation methods on action classification for domain transfer Emily Kim, Riyaz Panjwani link
Sliding attributes for GANs Daria Mashanova, Nishanth Thumbavanam Arun link
SemSynSin: end-to-end visual synthesis conditioned on semantic information Ingrid Navarro, Suann Chi link
A Study of Deep Learning-based 3D Point Clouds Reconstruction from 2D frames Dule Shu link
CGAN performance on Lego parts Alex Strasser link
Prompting GANs into Feed-Forward Energy-Based Models Chen Wu link
3D human texture synthesis Yutian Lei, Hao Wu link
Image compression with GAN Yida Wu link
Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering GANs Jason Xu, Emma Liu, Joyce Zhang link
Reproducing the Results of EG3D: Efficient Geometry-aware 3D GANs Jeff Tan link
NeRF in the wild Yuchen(Joshua) Cao link
Arbitrary style transfer on the OAK-1 Antioch Sanders link

Congratulations to all students for their amazing works!


Welcome to the final project for the class. The purpose is to show us something novel based on the materials we cover in the class. You can try a new modification of a method, a particularly novel application, or a close analysis of the properties of an existing method. We’ll read over your project proposals and give feedback on them early on so that we can get awesome results on cool problems! Feel free to come to our office hours to discuss the progress and challenges over the rest of the semester. We’re happy to help!

Group Policy

You can work in groups of 1-3 people. We’ll expect the standard of work to be roughly proportional to the number of members in your group. In other words, larger groups will be graded to a somewhat higher standard as far as the scale of the project attempted and the amount of work completed.

Important Dates

  • 3/28: Project Proposal Due
  • 4/27: Presentation Date
  • 5/09: Project Code and Website Due

Note: We will not allow late days on the project.

Project Proposal

We’d like to see a couple of paragraphs describing what you want to do for your project. Be sure to describe the end output, technique, novelty, dataset usage, and action plan. Include a couple of sentences placing your project proposal in context among related works. Submit this work as a pdf file to canvas. Feel free to include images or your hand-drawn figures. The page limit is two pages, but one page should be more than enough.


You’ll need to give a 5-minute presentation about your project in class. We’ll announce the time for this soon, but you should give a quick presentation that offers an overview of the method and data and shows us the cool outputs of your work! If you can’t make the time we announce, we’ll ask you to submit an equivalent video.

Code and Website Submission

You’ll need to submit (1) the code for your project to canvas and (2) a website in the project directory of your website for the course as you did for other projects. This time, we’d really want to see through the description of the method, outputs of comparison methods (if applicable), the outputs of your algorithm, any math you do, and ablations if applicable. This will be the primary deliverable, and we encourage you all to do a good job with it, as you’ll be able to show people what you’ve made in a nicely presented way.

Good luck!