Search Results for author: Amit Raj

Found 8 papers, 3 papers with code

DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars

no code implementations29 Mar 2022 Amit Raj, Umar Iqbal, Koki Nagano, Sameh Khamis, Pavlo Molchanov, James Hays, Jan Kautz

In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.

Neural Rendering

Pixel-Aligned Volumetric Avatars

no code implementations CVPR 2021 Amit Raj, Michael Zollhofer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi

Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.

PVA: Pixel-aligned Volumetric Avatars

no code implementations7 Jan 2021 Amit Raj, Michael Zollhoefer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi

Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.

ANR: Articulated Neural Rendering for Virtual Avatars

no code implementations CVPR 2021 Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images.

Neural Rendering

Kernel Mean Matching for Content Addressability of GANs

1 code implementation14 May 2019 Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf

We propose a novel procedure which adds "content-addressability" to any given unconditional implicit model e. g., a generative adversarial network (GAN).

Image Generation

SwapNet: Garment Transfer in Single View Images

1 code implementation ECCV 2018 Amit Raj, Patsorn Sangkloy, Huiwen Chang, Jingwan Lu, Duygu Ceylan, James Hays

Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body.

 Ranked #1 on Virtual Try-on on FashionIQ (using extra training data)

Virtual Try-on

Deep Forward and Inverse Perceptual Models for Tracking and Prediction

no code implementations31 Oct 2017 Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots

We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics.

Image Generation

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