Company: Qualcomm Canada ULC
Job Area: Interns Group, Interns Group
Interim Engineering Intern - SW
Qualcomm Overview:
Qualcomm is a company of inventors that unlocked 5G ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in.
General Summary:
Before there were smartphones or smart cities, before autonomous cars or 360 virtual reality videos, there was our technology. Headquartered in San Diego, for over 30 years Qualcomm inventions have inspired others to make the impossible, possible. From 5G to artificial intelligence, from IoT to automotive and extended reality applications, Qualcomm is inventing the technologies of an intelligently connected future, spearheading research efforts for the next global wireless standard, and collaborating with industry leaders in the wireless value chain to make this future a commercial reality.
The field of generative modeling is evolving rapidly, with video diffusion emerging as a powerful paradigm for high-quality, temporally consistent video synthesis. This internship offers an exciting opportunity to explore the applicability of video diffusion models [1, 2, 3] to various downstream applications, such as image editing [6, 7] or facial reenactment [4, 5].
Responsibilities:
Investigate novel architectures and training strategies to improve video generation quality and efficiency.
Explore conditioning mechanisms to extend existing video diffusion models to downstream applications.
Generate or curate use-case specific datasets to support model development and evaluation.
Implement and benchmark baseline models for comparative analysis, including thorough ablation studies.
Optimize model performance with a focus on computational efficiency and scalability.
The research conducted in this internship is aimed at advancing the field of generative modeling using video diffusion, with the expectation of contributing to paper submissions at top-tier conferences in the field.
[1] LTXVideo: Realtime video latent diffusion, https://arxiv.org/abs/2501.00103
[2] HunyuanVideo: A Systematic Framework For Large Video Generative Models, https://arxiv.org/abs/2412.03603
[3] Wan: Open and Advanced Large-Scale Video Generative Models, https://arxiv.org/abs/2503.20314
[4] LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control, https://arxiv.org/abs/2407.03168
[5] Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control, https://arxiv.org/abs/2405.12970
[6] FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space, https://arxiv.org/abs/2506.15742
[7] InstructPix2Pix: Learning to Follow Image Editing Instructions, https://arxiv.org/abs/2211.09800
Programming Languages:
Python
Minimum Qualification:
Pytorch
Neural network architecture development and evaluation
Computer Vision
Educational Requirements:
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