DiCoMoGAN is a video editing framework for manipulating videos with natural language, aiming to perform local and semantic edits on a video clip to alter the appearances of an object of interest. Our GAN architecture allows for better utilization of multiple observations by disentangling content and motion to enable controllable semantic edits. To this end, we introduce two tightly coupled networks: (i) a representation network for constructing a concise understanding of motion dynamics and temporally invariant content, and (ii) a translation network that exploits the extracted latent content representation to actuate the manipulation according to the target description. Our qualitative and quantitative evaluations demonstrate that DiCoMoGAN significantly outperforms existing frame-based methods, producing temporally coherent and semantically more meaningful results.
For more details, you can reach the paper and project page
DiCoMOGAN is coded with PyTorch
It requires the following installations:
python 3.8.3
pytorch (1.7.1)
cuda 11.1
Given a dataset root path in which there are folders containing frames and video list and corresponding descriptions, you can train your own model. In the following, we are showing main structure of dataset folder:
/dataset_root/<video folders>
/dataset_root/train_video.txt
/dataset_root/train_video_descriptions.txt
/dataset_root/test_video.txt
/dataset_root/test_video_descriptions.txt
First, we use the 3D Shapes dataset which is proposed for learning and assessing factors of variation from data. This dataset has 480K images of 64 × 64 resolution. There are 6 ground truth independent latent factors. They are floor color, wall color, object color, scale, shape and orientation. For our purpose, we build simple text descriptions which covers object related latent factors object color, scale and shape, e.g. “There is a big blue capsule.”. To prevent scale ambiguity, we remove two elements of the scale factor which is of length 8, originally. In that case, “small”, “medium” and “big” in the descriptions correspond to the first two, middle two and the last two values, respectively. Moreover, we consider the orientation factor as a dynamic dimension taking 15 different values. We have 19.2K train and 4.8K test videos with 15 frames and simple text descriptions for each video.
If you use this dataset in your work, please cite related work in 3D Shapes dataset.
We collected our dataset from raw videos present in the website of an online clothing retailer by searching products in the cardigans, dresses, jackets, jeans, jump- suits, shorts, skirts, tops and trousers categories. Figure given below illustrates these nine garment types and the variety within each category with some samples. There are 3178 video clips (approximately 109K distinct frames), which we split into 2579 for training and 598 for testing. Garment descriptions. We obtained textual descriptions of the clothes from the headers of the html files by extracting the hierarchy and info sections of the items. These product descriptions give details about its color, material properties and design details. We only manually removed the repetitive or ill-suited words from the descriptions.
Please send us a request e-mail to download dataset.
If you use this dataset in your work, please cite our work.
You can train DiCoMoGAN using the following scripts for Fashion Video and 3dShapes datasets:
run_GAN_fashion_textVAEODE.sh
run_GAN_3dshapes_textVAEODE.sh
You can test DiCoMoGAN using test.sh script. You can reach pre-trained Fashion model and 3D Shapes model.
Please cite the following paper if you use DiCoMOGAN and proposed Fashion Dataset.
@inproceedings{Karacan_2022_BMVC,
author = {Levent Karacan and Tolga Kerimoğlu and İsmail Ata İnan and Tolga Birdal and Erkut Erdem and Aykut Erdem},
title = {Disentangling Content and Motion for Text-Based Neural Video Manipulation},
booktitle = {British Machine Vision Conference (BMVC)},
year = {2022}}