Search Results for author: Shagun Uppal

Found 13 papers, 6 papers with code

Dexterous Functional Grasping

no code implementations5 Dec 2023 Ananye Agarwal, Shagun Uppal, Kenneth Shaw, Deepak Pathak

However, this task requires both a complex understanding of functional affordances as well as precise low-level control.

Emotionally Enhanced Talking Face Generation

1 code implementation21 Mar 2023 Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Yi Yu, Yifang Yin, Rajiv Ratn Shah

To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing.

Talking Face Generation Talking Head Generation

Emotional Talking Faces: Making Videos More Expressive and Realistic

no code implementations ACM Multimedia Asia 2022 Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Dhroov Goel, Sakshat Mali, Yi Yu, Yifang Yin, Rajiv Ratn Shah

Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields.

Talking Face Generation

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

1 code implementation11 Nov 2021 I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert, Youngwoon Lee

Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations.

Imitation Learning Motion Planning +3

Multimodal Research in Vision and Language: A Review of Current and Emerging Trends

no code implementations19 Oct 2020 Shagun Uppal, Sarthak Bhagat, Devamanyu Hazarika, Navonil Majumdar, Soujanya Poria, Roger Zimmermann, Amir Zadeh

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data.

DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors

1 code implementation10 Jun 2020 Sarthak Bhagat, Vishaal Udandarao, Shagun Uppal

Disentangling the underlying feature attributes within an image with no prior supervision is a challenging task.

Attribute Contrastive Learning +1

C3VQG: Category Consistent Cyclic Visual Question Generation

1 code implementation15 May 2020 Shagun Uppal, Anish Madan, Sarthak Bhagat, Yi Yu, Rajiv Ratn Shah

In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.

Natural Questions Question Generation +1

Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders

1 code implementation ECCV 2020 Sarthak Bhagat, Shagun Uppal, Zhuyun Yin, Nengli Lim

Our experiments show that the combination of the improved representations with the novel loss function enable MGP-VAE to outperform the baselines in video prediction.

Gaussian Processes Video Prediction

Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning

no code implementations22 Jul 2019 Ankita Shukla, Sarthak Bhagat, Shagun Uppal, Saket Anand, Pavan Turaga

Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation.

Disentanglement

Geometry of Deep Generative Models for Disentangled Representations

no code implementations19 Feb 2019 Ankita Shukla, Shagun Uppal, Sarthak Bhagat, Saket Anand, Pavan Turaga

We use several metrics to compare the properties of latent spaces of disentangled representation models in terms of class separability and curvature of the latent-space.

Representation Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.