Search results
When an international terrorist cult threatens global political stability and kidnaps a fellow agent, Ace Sniper Brandon Beckett and the newly-formed Global Response & Intelligence Team – or G.R.I.T. – led by Colonel Stone must travel across the world to Malta, infiltrate the cult, and take out its leader to free Lady Death and stop the ...
- (105)
- 16
- Oliver Thompson
- 92 min
Aug 23, 2023 · When an international terrorist cult threatens global political stability and kidnaps a fellow agent, Ace Sniper Brandon Beckett (Chad Micha Sniper: G.R.I.T. in US theaters September 26, 2023 starring Chad Michael Collins, Ryan Robbins, Luna Fujimoto, Josh Brener.
- (4)
- Eugenia Caruso
- Oliver Thompson
View full company info for Git International Film. 1. L'assassino è costretto ad uccidere ancora (1975) Not Rated | 90 min | Crime, Horror, Thriller. 6.4. Rate this. Giorgio is a greedy adulterer who makes a deal with a serial killer to dispose of his wealthy wife, Nora.
- Overview
- Web Demo
- Change Log
- Installation
- Pre-trained Models
- Running the Codes
- Datasets
- Training
- Evaluation on Benchmarks
- Citation
Website | Paper | Google AI Blog | Tensorflow Hub Colab | YouTube
The official Tensorflow 2 implementation of our high quality frame interpolation neural network. We present a unified single-network approach that doesn't use additional pre-trained networks, like optical flow or depth, and yet achieve state-of-the-art results. We use a multi-scale feature extractor that shares the same convolution weights across the scales. Our model is trainable from frame triplets alone.
FILM: Frame Interpolation for Large Motion
Fitsum Reda1, Janne Kontkanen1, Eric Tabellion1, Deqing Sun1, Caroline Pantofaru1, Brian Curless1,2
1Google Research, 2University of Washington
In ECCV 2022.
Integrated into Hugging Face Spaces 🤗 using Gradio. Try out the Web Demo:
Try the interpolation model with the replicate web demo at
Try FILM to interpolate between two or more images with the PyTTI-Tools at
An alternative Colab for running FILM on arbitrarily more input images, not just on two images,
•Nov 28, 2022: Upgrade eval.interpolator_cli for high resolution frame interpolation. --block_height and --block_width determine the total number of patches (block_height*block_width) to subdivide the input images. By default, both arguments are set to 1, and so no subdivision will be done.
•Mar 12, 2022: Support for Windows, see WINDOWS_INSTALLATION.md.
•Get Frame Interpolation source codes
•Optionally, pull the recommended Docker base image
•If you do not use Docker, set up your NVIDIA GPU environment with:
•Anaconda Python 3.9
•CUDA Toolkit 11.2.1
•cuDNN 8.1.0
•Create a directory where you can keep large files. Ideally, not in this directory.
•Download pre-trained TF2 Saved Models from google drive and put into .
One mid-frame interpolation
To generate an intermediate photo from the input near-duplicate photos, simply run: This will produce the sub-frame at t=0.5 and save as 'photos/output_middle.png'.
Many in-between frames interpolation
It takes in a set of directories identified by a glob (--pattern). Each directory is expected to contain at least two input frames, with each contiguous frame pair treated as an input to generate in-between frames. Frames should be named such that when sorted (naturally) with natsort, their desired order is unchanged. You will find the interpolated frames (including the input frames) in 'photos/interpolated_frames/', and the interpolated video at 'photos/interpolated.mp4'. The number of frames is determined by --times_to_interpolate, which controls the number of times the frame interpolator is invoked. When the number of frames in a directory is num_frames, the number of output frames will be (2^times_to_interpolate+1)*(num_frames-1).
Creating a TFRecord
The training and benchmark evaluation scripts expect the frame triplets in the TFRecord storage format. We have included scripts that encode the relevant frame triplets into a tf.train.Example data format, and export to a TFRecord file. You can use the commands python3 -m datasets.create_ _tfrecord --help for more information. For example, run the command below to create a TFRecord for the Middlebury-other dataset. Download the images and point --input_dir to the unzipped folder path. The above command will output a TFRecord file with 3 shards as @3.
Below are our training gin configuration files for the different loss function:
To launch a training, simply pass the configuration filepath to the desired experiment.
By default, it uses all visible GPUs for training. To debug or train on a CPU, append --mode cpu.
•When training finishes, the folder structure will look like this:
Below, we provided the evaluation gin configuration files for the benchmarks we have considered:
To run an evaluation, simply pass the configuration file of the desired evaluation dataset.
If a GPU is visible, it runs on it.
The above command will produce the PSNR and SSIM scores presented in the paper.
If you find this implementation useful in your works, please acknowledge it appropriately by citing:
GIT 2024. With great pleasure, the Indian Institute of Management Kozhikode announces the 5th edition of the International Conclave on "Globalizing Indian Thought (GIT 2024)", which will expand its global reach and will be conducted in London during October 24-25, 2024.
Git International Film. New Movies. This Dinner Will End Release Date 2023-10-27. Half Serious, Half Kidding Release Date 2023-05-22. Mickey & Friends 10 Classic ...
Git International Film. Company; Horror Thriller. Horror - 1 Movies. Genre Average: 5.06. The Killer Must Kill Again ...
