[OPEN][RI-IML_2019-CG-DM-VP-007] Deep 3D Object Localization & Tracking for Real / Virtual Fusion

To fuse virtual objects with real content (or the opposite) it is necessary to know the parameters of the virtual and real cameras. In particular, the 3D localization of the real camera and the real object needs to be computed and tracked to ensure temporal consistency and fast computing. Current solutions rely on visual pattern tracking. More recent technologies consider fusing information from various sensors as used for automotive applications, such as accelerometer, gyroscope, GPS, LIDAR, radar…The goal of the internship is to improve current algorithms and propose innovative solutions for filtering and fusing the various signals. Deep learning could also be considered as an improvement of existing techniques.

Skills  : Applied Mathematics, Signal Processing, Programming, Computer Vision, Machine/Deep Learning.

Keywords  : Localization, sensors, filtering, machine learning, VFX, movies.

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-HOME_2019-DM-001] Weakly supervised multiple instance learning

We have been developing sensors that can detect the presence of people based on supervised learning, for instance geophone, microphone… However, a sensor does not always port well in a new environment, and it is not easy to adapt it to a new context, because one cannot expect that the user will provide a precise ground truth for each step or for each trace of presence in any sensor. We propose therefore to study the feasibility of using a new learning paradigm for the home setting: multiple instance learning. This learning method is inherently weakly supervised: it needs a ground truth expressing that “there is nothing in a series of observations”, or “there is something in a range of observations”, even if one is not able to say precisely where it is possible to detect something. With such an algorithm the user has just to say: “I get out of the house” or “I am back”, or this can be automatically detected from its phone for instance. When there is nobody the system learns the background noise of the house, and when there is somebody it learns the specific noises that somebody will cause on the sensor. Notice that the background noises can comprise noises caused by neighbors (in a condo), or by pets, or by traffic outside. Examples of signals to exploits: geophones, microphones, low resolution cameras, discrete sensors. The objective of the training period is to develop a new algorithm, experiment and publish results in a research paper.

Skills  : Signal Processing, Machine Learning, Neural Networks. Python programming. Knowledge of Scikit Learn. Able to read and write research papers. Fluent in written English. Interested in mathematics and signal processing.

Keywords  : Learning algorithm, weakly supervised algorithms

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-HOME_2019-DM-NW-003] Network congestion control based on deep reinforcement learning

Congestion control is one fundamental building block of networking protocols. Over the last decades, network researchers have designed a wide variety of congestion control algorithms that each target a specific environment or application (e.g. datacenter, wifi, video…) or optimize a specific aspect in networking performance (bufferbloat, latency, bandwidth...). Most congestion control schemes were designed as classical deterministic algorithms based on expert knowledge using hardwired and predefined control responses. Only recently machine-learning based congestion control schemes have been proposed by the community. These efforts are quite recent and do not employ the latest deep reinforcement learning techniques. Deep Reinforcement Learning denotes the use of deep learning, a powerful class of learning algorithm, to develop reinforcement learning algorithms: algorithms that attempt to learn how to control a system optimally. These algorithms have been in a spotlight lately as they have achieved impressive results in a variety of tasks such as beating human experts at Go, competing against them in cooperative video games or reducing energy consumption by 40% in data centers. Considering the recent advances in Deep Reinforcement Learning and the availability of emulation and evaluation platforms for congestion control schemes, we believe that there is room to design a congestion control scheme based on deep reinforcement learning. The objective of this internship is therefore to design and evaluate deep reinforcement learning based congestion control schemes. Technicolor being a home gateway and settop-box manufacturer a specific focus in the evaluation of the proposed algorithms will be home environments.

Skills  : .

Keywords  : Deep learning, reinforcement learning algorithm

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-IML_2019-CG-DM-HCI-008] Reconstruction of Personalized 3D Human Body Model

This internship will focus on the 3D reconstruction of the human body. Virtual body enables to generate personalized avatars which are more and more required in VR, AR, gaming and many other virtual applications. It helps increasing the embodiment and preventing from cybersickness. The intern will be included in the Immersive Lab within the Virtual Production group at Technicolor Rennes. Several tools and pieces of software developed by the team are available and will be improved. The work will consist in: (1) study the state of the art around 3D reconstruction of human avatars [1], and in particular evaluate a technique based on RGB-D video [2], (2) improve the existing camera rig and reconstruction algorithm in order to provide at the end a realistic digital double of the people.

[1] J. Achenbach, T. Waltemate, M. Erich Latoschik, and M. Botsch. 2017. Fast generation of realistic virtual humans. In Proceedings of the 23rd ACM-VRST '17
[2] Alldieck, T., Magnor, M. A., Xu, W., Theobalt, C., & Pons-Moll, G. (2018). Video Based Reconstruction of 3D People Models. arXiv preprint arXiv:1803.04758.

Skills  : Computer graphics, Python, Deep Learning, Math (optimization and geometry), English, motivated by research.

Keywords  : Geometry Processing, 3D reconstruction, deep learning

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-CG-DM-018] Deep Geometry

3D models are often represented by very large numbers of points or triangles. This makes both storage and image synthesis inefficient, and therefore requires high-end GPUs to produce images. This internship will investigate the possibility of replacing large and detailed 3D models, normally represented as either triangle meshes or point clouds, with deep neural networks. Such networks could then be employed by a renderer to efficiently produce images.

Skills  : machine learning, 3D rendering, Python, C++.

Keywords  : .machine learning, 3D rendering, Python, C++.

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-DM-HCI-016] Gesture Recognition by Deep Learning

Action and Gesture recognition have a growing interest in several application domain essentially in human machine interaction like in automotive, games and digital TV user interface. The goal of this internship is to explore and propose a new framework based on Neural Network to achieve a gesture recognition for digital TV application where features are extracted as well in the spatial and temporal domain.

Skills  : - Matlab/Python/C programming, ideally with image processing expertise - Ability to write well-structure and documented code - Good written and spoken English - Excellent team working skills as the internship forms a part of a larger project, involving many team members - Ability to work independently

Keywords  : .Machine Learning, Deep Learning, SVM, Clustering

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-DM-VP-015] Speeding-up video coding with deep-learning

The topic of this internship is the development of Deep Learning based methods to speed-up/improve state-of –the-art video codec (namely VCC/H.266). The goal of the internship is to tackle the combinatory problem arising with the new codecs, especially because of the enhanced block topologies available in the codec. The goal is to manage combinatory reduction without decreasing the codec performance. Deep Learning based methods have already proved their efficiency for intra coding mode (see http://phenix.int-evry.fr/jvet/doc_end_user/documents/10_San%20Diego/wg11/JVET-J0034-v2.zip). Many extensions are possible, especially regarding the inter coding mode, dealing with motion field segmentation. The candidate should be familiar with current machine learning software packages and have a good background in image processing in general.

Skills  : Skills: (deep) learning algorithms and software, programming (C++/python), motion estimation

Keywords  : video codec, machine learning, motion segmentation, image processing

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-CG-CV-DM-020] Aging 3D Character

In VFX production (film or advertisement) the need to reconstruct 3d actor’s face from video input is increasing. Over the last decade, the technology that pulls out a 3d facial model from a flat image has been improved significantly, while fine-scale mesoscopic detail may miss out. With the recent growth of deep learning techniques, we believe that morphing a 3d character’s age would be possible, by learning “(de-)aging” from data. Done automating this pipeline brings benefits to the VFX industry, reducing manual labour. Our research team is based in Rennes & New York. And collaborates with engineers and artists located at The Mill, New York.

Skills  : Machine learning, Deep-learning, Computer Graphics, Computer Vision, Python, PyTorch, Maya.

Keywords  : .deep network, visual effects, facial rig, 3d reconstruction, shape from shading

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-CV-DM-VP-021] Deep Learning for Rotoscoping

Very recently deep learning approaches allowed developing very efficient approaches in various fields (e.g., image/video processing, computer vision, audio processing). This internship proposal targets the development of deep learning approaches for high-end visual effects. In this context, both the interaction with a user (roto artist) and the efficient propagation of the effect throughout a whole sequence are keys to achieve both a highly accurate and efficient process. The proposal will target these two aspects, interaction and spatio-temporal propagation in the context of deep learning segmentation and matting methods. Resulting algorithms might be integrated in a professional VFX software to help the colorists.

Skills  : machine learning, deep learning, computer vision, video/image processing, PyTorch, TensorFlow or Keras deep learning frameworks, Python or C++.

Keywords  : .machine learning (deep learning), video processing, computer vision, interaction, segmentation, tracking, rotoscoping, matting

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-CV-DM-VP-022] Deep learning for 3D face rig

This internship proposal targets development of deep learning approaches for high-end visual effects (generation and animation of 3D avatars for film studios). Recent techniques, such as MoFA, achieve good 3D face rig reconstruction from still images and videos. However, these face rigs only cover skin parts, missing eyes and mouth interior. To improve this, we propose to study the use of Generative Adversarial Networks (GAN) to fill these parts.

Skills  : machine learning, deep learning, computer vision, video/image processing, PyTorch, Python

Keywords  : .machine learning, deep learning, video processing, computer vision

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

[OPEN][RI-ISL_2019-CG-CV-DM-024] Extraction of quadrupeds motion parameters from video

The goal of the internship is to apply deep learning techniques for the extraction of motion parameters of quadrupeds from video. In order to cope with the lack of ground truth, the approach will build upon both weakly and unsupervised learning. Biomechanical knowledge or possibly tiny manual annotation dataset might also be exploited. The motivation for this work is to develop a statistical model of the motion of some quadrupeds in order to synthesize plausible animation.

The context of this work is the VFX workflow for animated movies industry. This work is part of an effort to automatize the currently very manual process.

The objective is to design the model and the learning methodology for extracting the 3D coordinates of a moving quadruped in video.

The expected outcome of the internship are :
- A model with the quantitative evaluation of its performance
- A description of the approach which might lead to a publication or patent
- A demo which will visually display the produced 3D animation

References
- Zhou, Xingyi, Qixing Huang, Xiao Sun, Xiangyang Xue, and Yichen Wei. "Weakly-supervised Transfer for 3D Human Pose Estimation in the Wild." arXiv preprint arXiv:1704.02447 (2017).
- Newell, Alejandro, Kaiyu Yang, and Jia Deng. "Stacked hourglass networks for human pose estimation." In European Conference on Computer Vision, pp. 483-499. Springer International Publishing, 2016.

Skills  : deep learning

Keywords  : deep learning

 

This internship is located in Rennes, France. If interested, please apply at stage.technicolor@technicolor.com  by sending us your resume and a cover letter with the internship reference in the email subject line.

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