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Prafull Sharma. PhD Student @ CSAIL, MIT. Research Interests: Computer Vision, Graphics, Machine Learning. Contact: prafull (at) mit (dot) edu. Bio. I am a PhD student advised by Prof. William T. Freeman and Prof. Frédo Durand at the MIT Computer Science & Artificial Intelligence Laboratory.
- Materialistic: Selecting Similar Materials in Images
Prafull Sharma 1,2, Julien Philip 2, Michaël Gharbi 2,...
- Neural Groundplans: Persistent Neural Scene Representations
Prafull Sharma, Ayush Tewari, Yilun Du, Sergey Zakharov,...
- Alchemist: Parametric Control of Material Properties with ...
We propose a method to control material attributes of...
- Neural Groundplans: Persistent Neural Scene ... - Prafull Sharma
We present a method to map 2D image observations of a scene...
- Materialistic: Selecting Similar Materials in Images
Prafull Sharma. PhD Student @ CSAIL, MIT. Research Interests: Computer Vision, Graphics, Machine Learning. Contact: prafull (at) mit (dot) edu. Bio. I am a PhD student advised by Prof. William T. Freeman and Prof. Frédo Durand at the MIT Computer Science & Artificial Intelligence Laboratory.
Show more. PhD Student, MIT - Cited by 217 - Computer Vision - Computer Graphics - Machine Learning.
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with ...
View Prafull Sharma’s profile on LinkedIn, a professional community of 1 billion members. Absolute Studios · Producer · Experience: Absolute Studios · Education: infinity business school ·...
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We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.
Aug 30, 2021 · Prafull Sharma, Miika Aittala, Yoav Y. Schechner, Antonio Torralba, Gregory W. Wornell, William T. Freeman, Fredo Durand. We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room.