Search Results for author: Raja Bala

Found 7 papers, 2 papers with code

A Simple Strategy for Body Estimation from Partial-View Images

no code implementations14 Apr 2024 Yafei Mao, Xuelu Li, Brandon Smith, Jinjin Li, Raja Bala

Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation.

Position Virtual Try-on

MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation

1 code implementation12 Mar 2024 Yuelong Li, Yafei Mao, Raja Bala, Sunil Hadap

Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage.

Pose Estimation

Human Body Measurement Estimation with Adversarial Augmentation

no code implementations11 Oct 2022 Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora, Ming C. Lin, Javier Romero, Raja Bala

Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.

STRIVE: Scene Text Replacement In Videos

no code implementations ICCV 2021 Vijay Kumar B G, Jeyasri Subramanian, Varnith Chordia, Eugene Bart, Shaobo Fang, Kelly Guan, Raja Bala

Finally, the new text is transferred from the reference to remaining frames using a novel learned image transformation network that captures lighting and blur effects in a temporally consistent manner.

Style Transfer

Editing in Style: Uncovering the Local Semantics of GANs

2 code implementations CVPR 2020 Edo Collins, Raja Bala, Bob Price, Sabine Süsstrunk

Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image.

Disentanglement Image Generation

Semi-supervised Conditional GANs

no code implementations19 Aug 2017 Kumar Sricharan, Raja Bala, Matthew Shreve, Hui Ding, Kumar Saketh, Jin Sun

We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework.

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