Search Results for author: Guillaume Berger

Found 5 papers, 1 papers with code

HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation

no code implementations15 Jan 2024 Antoine Mercier, Ramin Nakhli, Mahesh Reddy, Rajeev Yasarla, Hong Cai, Fatih Porikli, Guillaume Berger

Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task.

Text to 3D

Efficient neural supersampling on a novel gaming dataset

no code implementations ICCV 2023 Antoine Mercier, Ruan Erasmus, Yashesh Savani, Manik Dhingra, Fatih Porikli, Guillaume Berger

Real-time rendering for video games has become increasingly challenging due to the need for higher resolutions, framerates and photorealism.

Super-Resolution

Is end-to-end learning enough for fitness activity recognition?

no code implementations14 May 2023 Antoine Mercier, Guillaume Berger, Sunny Panchal, Florian Letsch, Cornelius Boehm, Nahua Kang, Ingo Bax, Roland Memisevic

End-to-end learning has taken hold of many computer vision tasks, in particular, related to still images, with task-specific optimization yielding very strong performance.

Action Recognition Pose Estimation

On the effectiveness of task granularity for transfer learning

1 code implementation24 Apr 2018 Farzaneh Mahdisoltani, Guillaume Berger, Waseem Gharbieh, David Fleet, Roland Memisevic

We describe a DNN for video classification and captioning, trained end-to-end, with shared features, to solve tasks at different levels of granularity, exploring the link between granularity in a source task and the quality of learned features for transfer learning.

Classification General Classification +2

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